diff --git a/.api-extractor.json b/.api-extractor.json
index 33a6658d..83263724 100644
--- a/.api-extractor.json
+++ b/.api-extractor.json
@@ -1,7 +1,7 @@
 {
   "$schema": "https://developer.microsoft.com/json-schemas/api-extractor/v7/api-extractor.schema.json",
   "mainEntryPointFilePath": "types/lib/src/human.d.ts",
-  "bundledPackages": ["@tensorflow/tfjs-core", "@tensorflow/tfjs-converter"],
+  "bundledPackages": ["@tensorflow/tfjs-core", "@tensorflow/tfjs-data", "@tensorflow/tfjs-layers", "@tensorflow/tfjs-converter"],
   "compiler": {
     "skipLibCheck": false
   },
diff --git a/CHANGELOG.md b/CHANGELOG.md
index f22edc01..160492bd 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,8 +9,9 @@
   
 ## Changelog
   
-### **HEAD -> main** 2023/02/02 mandic00@live.com
+### **HEAD -> main** 2023/02/03 mandic00@live.com
 
+- add affectnet
 
 ### **3.0.5** 2023/02/02 mandic00@live.com
 
diff --git a/build.js b/build.js
index 5921c91a..029942d7 100644
--- a/build.js
+++ b/build.js
@@ -121,18 +121,22 @@ async function main() {
 
   // run api-extractor to create typedef rollup
   const extractorConfig = APIExtractor.ExtractorConfig.loadFileAndPrepare('.api-extractor.json');
-  const extractorResult = APIExtractor.Extractor.invoke(extractorConfig, {
-    localBuild: true,
-    showVerboseMessages: false,
-    messageCallback: (msg) => {
-      msg.handled = true;
-      if (msg.logLevel === 'none' || msg.logLevel === 'verbose' || msg.logLevel === 'info') return;
-      if (msg.sourceFilePath?.includes('/node_modules/')) return;
-      // if (apiExtractorIgnoreList.reduce((prev, curr) => prev || msg.messageId.includes(curr), false)) return; // those are external issues outside of human control
-      log.data('API', { level: msg.logLevel, category: msg.category, id: msg.messageId, file: msg.sourceFilePath, line: msg.sourceFileLine, text: msg.text });
-    },
-  });
-  log.state('API-Extractor:', { succeeeded: extractorResult.succeeded, errors: extractorResult.errorCount, warnings: extractorResult.warningCount });
+  try {
+    const extractorResult = APIExtractor.Extractor.invoke(extractorConfig, {
+      localBuild: true,
+      showVerboseMessages: false,
+      messageCallback: (msg) => {
+        msg.handled = true;
+        if (msg.logLevel === 'none' || msg.logLevel === 'verbose' || msg.logLevel === 'info') return;
+        if (msg.sourceFilePath?.includes('/node_modules/')) return;
+        // if (apiExtractorIgnoreList.reduce((prev, curr) => prev || msg.messageId.includes(curr), false)) return; // those are external issues outside of human control
+        log.data('API', { level: msg.logLevel, category: msg.category, id: msg.messageId, file: msg.sourceFilePath, line: msg.sourceFileLine, text: msg.text });
+      },
+    });
+    log.state('API-Extractor:', { succeeeded: extractorResult.succeeded, errors: extractorResult.errorCount, warnings: extractorResult.warningCount });
+  } catch (err) {
+    log.error('API-Extractor:', err);
+  }
   regExFile('types/human.d.ts', regEx);
   writeFile('export * from \'../types/human\';', 'dist/human.esm-nobundle.d.ts');
   writeFile('export * from \'../types/human\';', 'dist/human.esm.d.ts');
diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map
index a86cf756..827426cc 100644
--- a/demo/typescript/index.js.map
+++ b/demo/typescript/index.js.map
@@ -1,7 +1,7 @@
 {
   "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 * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations\n\nconst humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior\n  // backend: 'webgpu',\n  modelBasePath: '../../models',\n  filter: { enabled: true, equalization: false, flip: false },\n  face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n  body: { enabled: true },\n  hand: { enabled: true },\n  object: { enabled: false },\n  segmentation: { enabled: false },\n  gesture: { enabled: true },\n};\n\nconst human = new H.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;\nhuman.draw.options.drawPoints = true; // draw points on face mesh\n// human.draw.options.fillPolygons = true;\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, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 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  console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    if (timestamp.start === 0) timestamp.start = human.now();\n    // 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    fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n    fps.frames++;\n    fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n    if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n  }\n  timestamp.detect = human.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 = human.next(human.result); // smoothen result using last-known results\n    const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters\n    human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);\n\n    const opt: Partial<H.DrawOptions> = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n    await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n    perf(interpolated.performance); // write performance data\n  }\n  const now = human.now();\n  fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n  timestamp.draw = now;\n  status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n  const devices = await human.webcam.enumerate();\n  const id = devices[0].deviceId; // use first available video source\n  await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element\n  dom.canvas.width = human.webcam.width;\n  dom.canvas.height = human.webcam.height;\n  dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n    if (human.webcam.paused) await human.webcam.play();\n    else human.webcam.pause();\n  };\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('models stats:', human.models.stats());\n  log('models loaded:', human.models.loaded());\n  log('environment', human.env);\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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+  "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 * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations\n\nconst humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior\n  // backend: 'webgpu',\n  modelBasePath: '../../models',\n  filter: { enabled: true, equalization: false, flip: false },\n  face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n  body: { enabled: true },\n  hand: { enabled: true },\n  // face: { enabled: true, mesh: { enabled: false } },\n  // body: { enabled: false },\n  // hand: { enabled: false },\n  object: { enabled: false },\n  segmentation: { enabled: false },\n  gesture: { enabled: true },\n};\n\nconst human = new H.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;\nhuman.draw.options.drawPoints = true; // draw points on face mesh\n// human.draw.options.fillPolygons = true;\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, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 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  console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    if (timestamp.start === 0) timestamp.start = human.now();\n    // 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    fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n    fps.frames++;\n    fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n    if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n  }\n  timestamp.detect = human.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 = human.next(human.result); // smoothen result using last-known results\n    const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters\n    human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);\n\n    const opt: Partial<H.DrawOptions> = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n    await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n    perf(interpolated.performance); // write performance data\n  }\n  const now = human.now();\n  fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n  timestamp.draw = now;\n  status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n  const devices = await human.webcam.enumerate();\n  const id = devices[0].deviceId; // use first available video source\n  await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element\n  dom.canvas.width = human.webcam.width;\n  dom.canvas.height = human.webcam.height;\n  dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n    if (human.webcam.paused) await human.webcam.play();\n    else human.webcam.pause();\n  };\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('models stats:', human.models.stats());\n  log('models loaded:', human.models.loaded());\n  log('environment', human.env);\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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   "names": ["H", "width", "humanConfig", "human", "dom", "timestamp", "fps", "log", "msg", "status", "perf", "detectionLoop", "tensors", "drawLoop", "_a", "_b", "_c", "interpolated", "processed", "opt", "now", "webCam", "id", "main"]
 }
diff --git a/demo/typescript/index.ts b/demo/typescript/index.ts
index 0a0211be..2c046578 100644
--- a/demo/typescript/index.ts
+++ b/demo/typescript/index.ts
@@ -18,6 +18,9 @@ const humanConfig: Partial<H.Config> = { // user configuration for human, used t
   face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },
   body: { enabled: true },
   hand: { enabled: true },
+  // face: { enabled: true, mesh: { enabled: false } },
+  // body: { enabled: false },
+  // hand: { enabled: false },
   object: { enabled: false },
   segmentation: { enabled: false },
   gesture: { enabled: true },
diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js
index 6f994f21..dfcc55d8 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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     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var K5=`
+`;var Y5=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +26,7 @@ var at=Object.defineProperty;var Kn=Object.getOwnPropertyDescriptor;var Jn=Objec
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
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+`,K5=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +38,7 @@ var at=Object.defineProperty;var Kn=Object.getOwnPropertyDescriptor;var Jn=Objec
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
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+`,J5=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
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     vec2 coord = pixelate(vUv, size);
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   }
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+`,Q5=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +74,7 @@ var at=Object.defineProperty;var Kn=Object.getOwnPropertyDescriptor;var Jn=Objec
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
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+`,_5=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,8 +96,8 @@ var at=Object.defineProperty;var Kn=Object.getOwnPropertyDescriptor;var Jn=Objec
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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Tr=[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],vr=[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],Rr=[33,133,362,263,1,78,308],UA=Tr.map(e=>t2[e]),YA=vr.map(e=>t2[e]),KA=Rr.map(e=>t2[e]);function se(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var 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Ir=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],Nr=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],Lr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Or=[[474,475],[475,476],[476,477],[477,474]],Cr=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],Wr=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Dr=[[469,470],[470,471],[471,472],[472,469]],Fr=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function Ae(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var Br={lips:Ae(Ir),leftEye:Ae(Nr),leftEyebrow:Ae(Lr),leftIris:Ae(Or),rightEye:Ae(Cr),rightEyebrow:Ae(Wr),rightIris:Ae(Dr),faceOval:Ae(Fr)},Hr=Object.entries(Br).map(([e,t])=>t.map(n=>[n,e])).flat(),QA=new 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n=B.faceLabels.slice();if(e.score&&(n=G(n,"[score]",100*e.score)),e.gender&&(n=G(n,"[gender]",e.gender)),e.genderScore&&(n=G(n,"[genderScore]",100*e.genderScore)),e.age&&(n=G(n,"[age]",e.age)),e.distance&&(n=G(n,"[distance]",100*e.distance)),e.real&&(n=G(n,"[real]",100*e.real)),e.live&&(n=G(n,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let m=e.emotion.map(x=>`${Math.trunc(100*x.score)}% ${x.emotion}`);m.length>3&&(m.length=3),n=G(n,"[emotions]",m.join(" "))}(A=(s=e.rotation)==null?void 0:s.angle)!=null&&A.roll&&(n=G(n,"[roll]",ge(e.rotation.angle.roll))),(i=(a=e.rotation)==null?void 0:a.angle)!=null&&i.yaw&&(n=G(n,"[yaw]",ge(e.rotation.angle.yaw))),(d=(c=e.rotation)==null?void 0:c.angle)!=null&&d.pitch&&(n=G(n,"[pitch]",ge(e.rotation.angle.pitch))),(l=(y=e.rotation)==null?void 0:y.gaze)!=null&&l.bearing&&(n=G(n,"[gaze]",ge(e.rotation.gaze.bearing))),O0(t,n,e.box[0],e.box[1],B)}function Vr(e,t){var n,o,s,A;if((n=e.annotations)!=null&&n.leftEyeIris&&((o=e.annotations)!=null&&o.leftEyeIris[0])){t.strokeStyle=B.useDepth?"rgba(255, 200, 255, 0.3)":B.color,t.beginPath();let a=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,i=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),B.fillPolygons&&(t.fillStyle=B.useDepth?"rgba(255, 255, 200, 0.3)":B.color,t.fill())}if((s=e.annotations)!=null&&s.rightEyeIris&&((A=e.annotations)!=null&&A.rightEyeIris[0])){t.strokeStyle=B.useDepth?"rgba(255, 200, 255, 0.3)":B.color,t.beginPath();let 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     [gender] [genderScore]%
     age: [age] years
@@ -118,7 +118,7 @@ var at=Object.defineProperty;var Kn=Object.getOwnPropertyDescriptor;var Jn=Objec
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     [emotions]
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t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let s=t.strides[o],A=Math.floor((e+s-1)/s),a=Math.floor((e+s-1)/s),i=t.anchors[o];for(let c=0;c<A;c++){let d=s*(c+.5);for(let y=0;y<a;y++){let l=s*(y+.5);for(let m=0;m<i;m++)n.push([l,d])}}}return n}function N1(e,t,n,o,s){let A=Fe(t),a=e.map(x=>[A[0]/s*(x[0]-s/2),A[1]/s*(x[1]-s/2),x[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,c=i?j1(n,[0,0]):Ot,d=i?a.map(x=>[...xs(x,c),x[2]]):a,y=i?ds(o):Ot,l=j2(t),m=[Ee(l,y[0]),Ee(l,y[1])];return d.map(x=>[Math.trunc(x[0]+m[0]),Math.trunc(x[1]+m[1]),Math.trunc(x[2]||0)])}function L1(e,t,n,o){let s=t.landmarks.length>=pt.count?pt.symmetryLine:ve.symmetryLine,A=0,a=Ot,i;if(e&&M.kernels.includes("rotatewithoffset"))if(A=ls(t.landmarks[s[0]],t.landmarks[s[1]]),A&&A!==0&&Math.abs(A)>.2){let d=j2(t),y=[d[0]/n.shape[2],d[1]/n.shape[1]],l=r.image.rotateWithOffset(n,A,0,[y[0],y[1]]);a=j1(-A,d),i=Lt(t,l,[o,o]),r.dispose(l)}else i=Lt(t,n,[o,o]);else i=Lt(t,n,[o,o]);return[A,a,i]}var ys=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},O1=(e,t)=>{let n=ys(e),o=Fe(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var C1=6,fs=1.4,G0,Wt=null,le=0,Be=null,He=()=>le;async function W1(e){var t;return M.initial&&(G0=null),G0?e.debug&&h("cached model:",G0.modelUrl):G0=await L((t=e.face.detector)==null?void 0:t.modelPath),le=G0.executor&&G0.inputs[0].shape?G0.inputs[0].shape[2]:256,Be=r.scalar(le,"int32"),Wt=r.tensor2d(I1(le)),G0}function ms(e){if(!Wt||!Be)return r.zeros([0,0]);let t={};t.boxStarts=r.slice(e,[0,1],[-1,2]),t.centers=r.add(t.boxStarts,Wt),t.boxSizes=r.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=r.div(t.boxSizes,Be),t.centersNormalized=r.div(t.centers,Be),t.halfBoxSize=r.div(t.boxSizesNormalized,O.tf2),t.starts=r.sub(t.centersNormalized,t.halfBoxSize),t.ends=r.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=r.mul(t.starts,Be),t.endNormalized=r.mul(t.ends,Be);let n=r.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>r.dispose(t[o])),n}async function D1(e,t){var i,c,d,y,l,m;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=r.image.resizeBilinear(e,[le,le]),n.div=r.div(n.resized,O.tf127),n.normalized=r.sub(n.div,O.tf05);let o=G0==null?void 0:G0.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let x=o.sort((u,f)=>u.size-f.size);n.concat384=r.concat([x[0],x[2]],2),n.concat512=r.concat([x[1],x[3]],2),n.concat=r.concat([n.concat512,n.concat384],1),n.batch=r.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=r.squeeze(o[0]):n.batch=r.squeeze(o);r.dispose(o),n.boxes=ms(n.batch),n.logits=r.slice(n.batch,[0,0],[-1,1]),n.sigmoid=r.sigmoid(n.logits),n.scores=r.squeeze(n.sigmoid),n.nms=await r.image.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((c=t.face.detector)==null?void 0:c.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0);let s=await n.nms.array(),A=[],a=await n.scores.data();for(let x=0;x<s.length;x++){let u=a[s[x]];if(u>(((y=t.face.detector)==null?void 0:y.minConfidence)||0)){let f={};f.bbox=r.slice(n.boxes,[s[x],0],[1,-1]),f.slice=r.slice(n.batch,[s[x],C1-1],[1,-1]),f.squeeze=r.squeeze(f.slice),f.landmarks=r.reshape(f.squeeze,[C1,-1]);let g=await f.bbox.data(),P={startPoint:[g[0],g[1]],endPoint:[g[2],g[3]],landmarks:await f.landmarks.array(),confidence:u},v=z1(P,[(e.shape[2]||0)/le,(e.shape[1]||0)/le]),p=L2(v,t.face.scale||fs),b=O2(p);b.size[0]>(((l=t.face.detector)==null?void 0:l.minSize)||0)&&b.size[1]>(((m=t.face.detector)==null?void 0:m.minSize)||0)&&A.push(b),Object.keys(f).forEach(j=>r.dispose(f[j]))}}return Object.keys(n).forEach(x=>r.dispose(n[x])),A}var w0,ce=0,ps=2.3,Dt=D0.leftEyeLower0,Ft=D0.rightEyeLower0,Ge={leftBounds:[Dt[0],Dt[Dt.length-1]],rightBounds:[Ft[0],Ft[Ft.length-1]]},Ve={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function V1(e){var t,n;return M.initial&&(w0=null),w0?e.debug&&h("cached model:",w0.modelUrl):w0=await L((t=e.face.iris)==null?void 0:t.modelPath),ce=w0!=null&&w0.executor&&((n=w0.inputs)!=null&&n[0].shape)?w0.inputs[0].shape[2]:0,ce===-1&&(ce=64),w0}function C2(e,t,n,o){for(let s=0;s<ut.length;s++){let{key:A,indices:a}=ut[s],i=D0[`${n}${A}`];if(!o||o.includes(A))for(let c=0;c<a.length;c++){let d=a[c];e[i[c]]=[t[d][0],t[d][1],(t[d][2]+e[i[c]][2])/2]}}}var us=e=>{let t=e[Ge.leftBounds[0]][2],n=e[Ge.rightBounds[0]][2];return t-n},B1=(e,t,n,o,s,A=!1)=>{let a=O2(L2(S1([e[n],e[o]]),ps)),i=Fe(a),c=r.image.cropAndResize(t,[[a.startPoint[1]/s,a.startPoint[0]/s,a.endPoint[1]/s,a.endPoint[0]/s]],[0],[ce,ce]);if(A&&M.kernels.includes("flipleftright")){let d=r.image.flipLeftRight(c);r.dispose(c),c=d}return{box:a,boxSize:i,crop:c}},H1=(e,t,n,o=!1)=>{let s=[];for(let A=0;A<Ve.numCoordinates;A++){let a=e[A*3],i=e[A*3+1],c=e[A*3+2];s.push([(o?1-a/ce:a/ce)*n[0]+t.startPoint[0],i/ce*n[1]+t.startPoint[1],c])}return{rawCoords:s,iris:s.slice(Ve.index)}},G1=(e,t,n)=>{let o=e[D0[`${n}EyeUpper0`][Ve.upperCenter]][2],s=e[D0[`${n}EyeLower0`][Ve.lowerCenter]][2],A=(o+s)/2;return t.map((a,i)=>{let c=A;return i===2?c=o:i===4&&(c=s),[a[0],a[1],c]})};async function Z1(e,t,n){if(!(w0!=null&&w0.executor))return e;let{box:o,boxSize:s,crop:A}=B1(e,t,Ge.leftBounds[0],Ge.leftBounds[1],n,!0),{box:a,boxSize:i,crop:c}=B1(e,t,Ge.rightBounds[0],Ge.rightBounds[1],n,!0),d=r.concat([A,c]);r.dispose(A),r.dispose(c);let y=w0.execute(d);r.dispose(d);let l=await y.data();r.dispose(y);let m=l.slice(0,Ve.numCoordinates*3),{rawCoords:x,iris:u}=H1(m,o,s,!0),f=l.slice(Ve.numCoordinates*3),{rawCoords:g,iris:P}=H1(f,a,i,!1),v=us(e);Math.abs(v)<30?(C2(e,x,"left",null),C2(e,g,"right",null)):v<1?C2(e,x,"left",["EyeUpper0","EyeLower0"]):C2(e,g,"right",["EyeUpper0","EyeLower0"]);let p=G1(e,u,"left"),b=G1(e,P,"right");return e.concat(p).concat(b)}async function q1(e,t){var A,a,i,c,d,y,l,m,x,u;let n={lips:await((a=(A=t.filter(f=>f.size===160))==null?void 0:A[0])==null?void 0:a.data()),irisL:await((c=(i=t.filter(f=>f.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((y=(d=t.filter(f=>f.size===142))==null?void 0:d[0])==null?void 0:y.data()),irisR:await((m=(l=t.filter(f=>f.size===10))==null?void 0:l[1])==null?void 0:m.data()),eyeR:await((u=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:u.data())};for(let f of Object.values(n))if(!f)return e;let o=Me.reduce((f,g)=>f+=e[g][2],0)/Me.length;for(let f=0;f<n.irisL.length/2;f++)e.push([n.irisL[2*f+0],n.irisL[2*f+1],o]);let s=Pe.reduce((f,g)=>f+=e[g][2],0)/Pe.length;for(let f=0;f<n.irisR.length/2;f++)e.push([n.irisR[2*f+0],n.irisR[2*f+1],s]);for(let f=0;f<n.eyeL.length/2;f++)e[Me[f]]=[n.eyeL[2*f+0],n.eyeL[2*f+1],e[Me[f]][2]];for(let f=0;f<n.eyeR.length/2;f++)e[Pe[f]]=[n.eyeR[2*f+0],n.eyeR[2*f+1],e[Pe[f]][2]];for(let f=0;f<n.lips.length/2;f++)e[o2[f]]=[n.lips[2*f+0],n.lips[2*f+1],e[o2[f]][2]];return e}var K0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},K=null,r2=0;async function U1(e,t){var c,d,y,l,m,x,u,f,g,P;let n=(((c=t.face.detector)==null?void 0:c.skipTime)||0)>T()-K0.timestamp,o=K0.skipped<(((d=t.face.detector)==null?void 0:d.skipFrames)||0);!t.skipAllowed||!n||!o||K0.boxes.length===0?(K0.boxes=await D1(e,t),K0.timestamp=T(),K0.skipped=0):K0.skipped++;let s=[],A=[],a=0,i=r2;for(let v=0;v<K0.boxes.length;v++){let p=K0.boxes[v],b=0,j,k={id:a++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([b,j,k.tensor]=L1((y=t.face.detector)==null?void 0:y.rotation,p,e,(l=t.face.mesh)!=null&&l.enabled?r2:He()),t.filter.equalization){let N=k.tensor?await x2(k.tensor):void 0;r.dispose(k.tensor),N&&(k.tensor=N)}if(k.boxScore=Math.round(100*p.confidence)/100,!((m=t.face.mesh)!=null&&m.enabled)||!(K!=null&&K.executor)){k.box=I2(p,e),k.boxRaw=N2(p,e),k.score=k.boxScore,k.size=p.size,k.mesh=p.landmarks.map(N=>[(p.startPoint[0]+p.endPoint[0])/2+N[0]*e.shape[2]/He(),(p.startPoint[1]+p.endPoint[1])/2+N[1]*e.shape[1]/He()]),k.meshRaw=k.mesh.map(N=>[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/i]);for(let N of Object.keys(ve))k.annotations[N]=[k.mesh[ve[N]]]}else if(!K)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((x=t.face.attention)!=null&&x.enabled&&!M.kernels.includes("atan2"))return t.face.attention.enabled=!1,r.dispose(k.tensor),s;let N=K.execute(k.tensor),X=await N.find(W=>W.shape[W.shape.length-1]===1).data();if(k.faceScore=Math.round(100*X[0])/100,k.faceScore<(((u=t.face.detector)==null?void 0:u.minConfidence)||1)){if(p.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=I2(p,e),k.boxRaw=N2(p,e),k.score=k.boxScore,k.mesh=p.landmarks.map(W=>[(p.startPoint[0]+p.endPoint[0])/2+(p.endPoint[0]+p.startPoint[0])*W[0]/He(),(p.startPoint[1]+p.endPoint[1])/2+(p.endPoint[1]+p.startPoint[1])*W[1]/He()]),k.meshRaw=k.mesh.map(W=>[W[0]/(e.shape[2]||1),W[1]/(e.shape[1]||1),(W[2]||0)/i]);for(let W of Object.keys(ve))k.annotations[W]=[k.mesh[ve[W]]]}}else{let W=N.find(q=>q.shape[q.shape.length-1]===1404),V=r.reshape(W,[-1,3]),J=await V.array();r.dispose(V),(f=t.face.attention)!=null&&f.enabled?J=await q1(J,N):(g=t.face.iris)!=null&&g.enabled&&(J=await Z1(J,k.tensor,r2)),k.mesh=N1(J,p,b,j,r2),k.meshRaw=k.mesh.map(q=>[q[0]/(e.shape[2]||0),q[1]/(e.shape[1]||0),(q[2]||0)/i]);for(let q of Object.keys(D0))k.annotations[q]=D0[q].map(f0=>k.mesh[f0]);k.score=k.faceScore;let U={...O1(k.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};k.box=I2(U,e),k.boxRaw=N2(U,e),A.push(U)}r.dispose(N)}k.score>(((P=t.face.detector)==null?void 0:P.minConfidence)||1)?s.push(k):r.dispose(k.tensor)}return K0.boxes=A,s}async function Y1(e){var t,n,o,s,A,a;return M.initial&&(K=null),(t=e.face.attention)!=null&&t.enabled&&(K!=null&&K.signature)&&Object.keys(((n=K==null?void 0:K.signature)==null?void 0:n.outputs)||{}).length<6&&(K=null),K?e.debug&&h("cached model:",K.modelUrl):(o=e.face.attention)!=null&&o.enabled?K=await L(e.face.attention.modelPath):K=await L((s=e.face.mesh)==null?void 0:s.modelPath),r2=K.executor&&((A=K==null?void 0:K.inputs)!=null&&A[0].shape)?(a=K==null?void 0:K.inputs)==null?void 0:a[0].shape[2]:256,K}var K1=Re,J1=n2;var Gt=[],d0,W2=[],Q1=0,_1=0,Ht=Number.MAX_SAFE_INTEGER,Vt=!1;async function $1(e){var t,n,o;return M.initial&&(d0=null),d0?e.debug&&h("cached model:",d0.modelUrl):(d0=await L((t=e.face.emotion)==null?void 0:t.modelPath),Vt=((o=(n=d0==null?void 0:d0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,Vt?Gt=["angry","disgust","fear","happy","neutral","sad","surprise"]:Gt=["angry","disgust","fear","happy","sad","surprise","neutral"]),d0}async function Zt(e,t,n,o){var a,i;if(!d0)return[];let s=Ht<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),A=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>T()-_1;return t.skipAllowed&&A&&s&&Q1===o&&W2[n]&&W2[n].length>0?(Ht++,W2[n]):(Ht=0,new Promise(async c=>{var y;let d=[];if((y=t.face.emotion)!=null&&y.enabled){let l={},m=d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[2]:0;if(t.face.emotion.crop>0){let u=t.face.emotion.crop,f=[[u,u,1-u,1-u]];l.resize=r.image.cropAndResize(e,f,[0],[m,m])}else l.resize=r.image.resizeBilinear(e,[m,m],!1);Vt?(l.mul=r.mul(l.resize,255),l.normalize=r.sub(l.mul,[103.939,116.779,123.68]),l.emotion=d0==null?void 0:d0.execute(l.normalize)):(l.channels=r.mul(l.resize,O.rgb),l.grayscale=r.sum(l.channels,3,!0),l.grayscaleSub=r.sub(l.grayscale,O.tf05),l.grayscaleMul=r.mul(l.grayscaleSub,O.tf2),l.emotion=d0==null?void 0:d0.execute(l.grayscaleMul)),_1=T();let x=await l.emotion.data();for(let u=0;u<x.length;u++)x[u]>(t.face.emotion.minConfidence||0)&&d.push({score:Math.min(.99,Math.trunc(100*x[u])/100),emotion:Gt[u]});d.sort((u,f)=>f.score-u.score),Object.keys(l).forEach(u=>r.dispose(l[u]))}W2[n]=d,Q1=o,c(d)}))}var p0,de=[],t3=0,n3=0,Xt=Number.MAX_SAFE_INTEGER;async function o3(e){var t;return M.initial&&(p0=null),p0?e.debug&&h("cached model:",p0.modelUrl):p0=await L((t=e.face.description)==null?void 0:t.modelPath),p0}function bs(e){let t=e.image||e.tensor||e;if(!(p0!=null&&p0.inputs[0].shape))return t;let n=r.image.resizeBilinear(t,[p0.inputs[0].shape[2],p0.inputs[0].shape[1]],!1),o=r.mul(n,O.tf255);return r.dispose(n),o}async function qt(e,t,n,o){var i,c,d,y;let s={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(p0!=null&&p0.executor))return s;let A=Xt<(((i=t.face.description)==null?void 0:i.skipFrames)||0),a=(((c=t.face.description)==null?void 0:c.skipTime)||0)>T()-t3;return t.skipAllowed&&A&&a&&n3===o&&((d=de==null?void 0:de[n])==null?void 0:d.age)>0&&((y=de==null?void 0:de[n])==null?void 0:y.genderScore)>0?(Xt++,de[n]):(Xt=0,new Promise(async l=>{var m;if((m=t.face.description)!=null&&m.enabled){let x=bs(e),u=p0==null?void 0:p0.execute(x);t3=T(),r.dispose(x);let g=await u.find(D=>D.shape[1]===1).data(),P=Math.trunc(200*Math.abs(g[0]-.5))/100;P>(t.face.description.minConfidence||0)&&(s.gender=g[0]<=.5?"female":"male",s.genderScore=Math.min(.99,P));let v=r.argMax(u.find(D=>D.shape[1]===100),1),p=(await v.data())[0];r.dispose(v);let j=await u.find(D=>D.shape[1]===100).data();s.age=Math.round(j[p-1]>j[p+1]?10*p-100*j[p-1]:10*p+100*j[p+1])/10,(Number.isNaN(g[0])||Number.isNaN(j[0]))&&h("faceres error:",{model:p0,result:u});let k=u.find(D=>D.shape[1]===1024),N=k?await k.data():[];s.descriptor=Array.from(N),u.forEach(D=>r.dispose(D))}de[n]=s,n3=o,l(s)}))}var Ze=.1,Ut=.5;function gs(e,t,n){let o=!1,s=n.length-1;for(let A=0;A<n.length;s=A++)n[A].y>t!=n[s].y>t&&e<(n[s].x-n[A].x)*(t-n[A].y)/(n[s].y-n[A].y)+n[A].x&&(o=!o);return o}async function s3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),s=[];for(let a of D0.silhouette)s.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});Ze&&Ze>0&&(s=s.map(a=>({x:a.x>.5?a.x+Ze:a.x-Ze,y:a.y>.5?a.y+Ze:a.y-Ze})));for(let a=0;a<t;a++)for(let i=0;i<n;i++)gs(a/t,i/t,s)||(o.set(Ut*o.get(0,i,a,0),0,i,a,0),o.set(Ut*o.get(0,i,a,1),0,i,a,1),o.set(Ut*o.get(0,i,a,2),0,i,a,2));return o.toTensor()}var x0,D2=[],Yt=Number.MAX_SAFE_INTEGER,A3=0,a3=0;async function i3(e){var t;return M.initial&&(x0=null),x0?e.debug&&h("cached model:",x0.modelUrl):x0=await L((t=e.face.antispoof)==null?void 0:t.modelPath),x0}async function Kt(e,t,n,o){var a,i;if(!(x0!=null&&x0.executor))return 0;let s=(((a=t.face.antispoof)==null?void 0:a.skipTime)||0)>T()-a3,A=Yt<(((i=t.face.antispoof)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&A3===o&&D2[n]?(Yt++,D2[n]):(Yt=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[x0!=null&&x0.inputs[0].shape?x0.inputs[0].shape[2]:0,x0!=null&&x0.inputs[0].shape?x0.inputs[0].shape[1]:0],!1),y=x0==null?void 0:x0.execute(d),l=(await y.data())[0];D2[n]=Math.round(100*l)/100,A3=o,a3=T(),r.dispose([d,y]),c(D2[n])}))}var y0,F2=[],Jt=Number.MAX_SAFE_INTEGER,c3=0,d3=0;async function x3(e){var t;return M.initial&&(y0=null),y0?e.debug&&h("cached model:",y0.modelUrl):y0=await L((t=e.face.liveness)==null?void 0:t.modelPath),y0}async function 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l={};l.crop=r.image.resizeBilinear(e,[z0.inputs[0].shape[2],z0.inputs[0].shape[1]],!1),l.data=z0.execute(l.crop);let m=await l.data.data();d=Array.from(m),Object.keys(l).forEach(x=>r.dispose(l[x]))}A5[n]=d,k3=o,w3=T(),c(d)})}var S0,i5=[],j3=0,I3=0,N3=Number.MAX_SAFE_INTEGER;async function L3(e){return M.initial&&(S0=null),S0?e.debug&&h("cached model:",S0.modelUrl):S0=await L(e.face.insightface.modelPath),S0}async function l5(e,t,n,o){var a,i;if(!(S0!=null&&S0.executor))return[];let s=N3<(((a=t.face.insightface)==null?void 0:a.skipFrames)||0),A=(((i=t.face.insightface)==null?void 0:i.skipTime)||0)>T()-I3;return t.skipAllowed&&A&&s&&j3===o&&i5[n]?(N3++,i5[n]):new Promise(async c=>{var y;let d=[];if((y=t.face.insightface)!=null&&y.enabled&&(S0!=null&&S0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[S0.inputs[0].shape[2],S0.inputs[0].shape[1]],!1),l.data=S0.execute(l.crop);let m=await l.data.data();d=Array.from(m),Object.keys(l).forEach(x=>r.dispose(l[x]))}i5[n]=d,j3=o,I3=T(),c(d)})}var Ms=e=>{let t=(l,m)=>Math.atan2(l[1]-m[1],l[0]-m[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,s=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),A=s?e.mesh[473]:e.mesh[468],a=s?[(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],i=s?[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]],c=[(a[0]-A[0])/i[0]-n[0],o*(A[1]-a[1])/i[1]-n[1]],d=Math.sqrt(c[0]*c[0]+c[1]*c[1]);return d=Math.min(d,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],c)+Math.PI/2)%Math.PI,strength:d}},C3=(e,t)=>{let n=f=>{let g=Math.sqrt(f[0]*f[0]+f[1]*f[1]+f[2]*f[2]);return f[0]/=g,f[1]/=g,f[2]/=g,f},o=(f,g)=>{let P=f[0]-g[0],v=f[1]-g[1],p=f[2]-g[2];return[P,v,p]},s=(f,g)=>{let 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V2(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function s2(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function _3(e,t,n){let o=t.shape[1],s=t.shape[2],A=[[e.startPoint[1]/o,e.startPoint[0]/s,e.endPoint[1]/o,e.endPoint[0]/s]];return r.image.cropAndResize(t,A,[0],n)}function $3(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.palmLandmarks.map(A=>[A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:s,confidence:e.confidence}}function Z2(e,t=1.5){let n=s2(e),o=V2(e),s=[t*o[0]/2,t*o[1]/2],A=[n[0]-s[0],n[1]-s[1]],a=[n[0]+s[0],n[1]+s[1]];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function X2(e){let t=s2(e),n=V2(e),s=Math.max(...n)/2,A=[t[0]-s,t[1]-s],a=[t[0]+s,t[1]+s];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function Ls(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function en(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Ls(n)}var J3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ue(e,t){let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}function Os(e,t){let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n}function Q3(e,t){let n=[],o=e.length;for(let s=0;s<o;s++){n.push([]);for(let A=0;A<o;A++)n[s].push(ue(e[s],Os(t,A)))}return n}function y5(e,t){let n=Math.cos(e),o=Math.sin(e),s=[[n,-o,0],[o,n,0],[0,0,1]],A=J3(t[0],t[1]),a=Q3(A,s),i=J3(-t[0],-t[1]);return Q3(a,i)}function tn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-ue(t[0],n),-ue(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]}function f5(e,t){return[ue(e,t[0]),ue(e,t[1])]}var 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o=r.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(s=>r.dispose(n[s])),o}normalizeLandmarks(t,n){let o={};o.reshape=r.reshape(t,[-1,7,2]),o.div=r.div(o.reshape,this.inputSizeTensor),o.landmarks=r.add(o.div,this.anchors[n]?this.anchors[n]:0);let s=r.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}async predict(t,n){var i;let o={};o.resize=r.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=r.div(o.resize,O.tf127),o.image=r.sub(o.div,O.tf1),o.batched=this.model.execute(o.image),o.predictions=r.squeeze(o.batched),o.slice=r.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=r.sigmoid(o.slice),o.scores=r.squeeze(o.sigmoid);let s=await o.scores.data();o.boxes=r.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await r.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((i=n.hand)==null?void 0:i.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let A=await o.nms.array(),a=[];for(let c of A){let d={};d.box=r.slice(o.norm,[c,0],[1,-1]),d.slice=r.slice(o.predictions,[c,5],[1,14]),d.norm=this.normalizeLandmarks(d.slice,c),d.palmLandmarks=r.reshape(d.norm,[-1,2]);let y=await d.box.data(),l=y.slice(0,2),m=y.slice(2,4),x=await d.palmLandmarks.array(),u={startPoint:l,endPoint:m,palmLandmarks:x,confidence:s[c]},f=$3(u,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);a.push(f),Object.keys(d).forEach(g=>r.dispose(d[g]))}return Object.keys(o).forEach(c=>r.dispose(o[c])),a}};var Ds=5,rn=1.65,sn=[0,5,9,13,17,1,2],Fs=0,Bs=2,An=0,U2=class{constructor(t,n){E(this,"handDetector");E(this,"handPoseModel");E(this,"inputSize");E(this,"storedBoxes");E(this,"skipped");E(this,"detectedHands");var o,s,A;this.handDetector=t,this.handPoseModel=n,this.inputSize=((A=(s=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:s[0].shape)==null?void 0:A[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(a=>a[0]),o=t.map(a=>a[1]),s=[Math.min(...n),Math.min(...o)],A=[Math.max(...n),Math.max(...o)];return{startPoint:s,endPoint:A}}getBoxForPalmLandmarks(t,n){let o=t.map(A=>f5([...A,1],n)),s=this.calculateLandmarksBoundingBox(o);return Z2(X2(s),Ds)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Z2(X2(n),rn);o.palmLandmarks=[];for(let s=0;s<sn.length;s++)o.palmLandmarks.push(t[sn[s]].slice(0,2));return o}transformRawCoords(t,n,o,s){let A=V2(n),a=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],i=t.map(x=>[a[0]*(x[0]-this.inputSize/2),a[1]*(x[1]-this.inputSize/2),a[2]*x[2]]),c=y5(o,[0,0]),d=i.map(x=>[...f5(x,c),x[2]]),y=tn(s),l=[...s2(n),1],m=[ue(l,y[0]),ue(l,y[1])];return d.map(x=>[Math.trunc(x[0]+m[0]),Math.trunc(x[1]+m[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,s,A=(n.hand.skipTime||0)>T()-An,a=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&A&&a&&(s=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(o=!0));let i=[];for(let c=0;c<this.storedBoxes.length;c++){let d=this.storedBoxes[c];if(d)if(n.hand.landmarks){let y=n.hand.rotation?en(d.palmLandmarks[Fs],d.palmLandmarks[Bs]):0,l=s2(d),m=[l[0]/t.shape[2],l[1]/t.shape[1]],x=n.hand.rotation&&M.kernels.includes("rotatewithoffset")?r.image.rotateWithOffset(t,y,0,m):t.clone(),u=y5(-y,l),f=o?this.getBoxForPalmLandmarks(d.palmLandmarks,u):d,g=_3(f,x,[this.inputSize,this.inputSize]),P=r.div(g,O.tf255);r.dispose(g),r.dispose(x);let[v,p]=this.handPoseModel.execute(P);An=T(),r.dispose(P);let b=(await v.data())[0];if(r.dispose(v),b>=n.hand.minConfidence/4){let j=r.reshape(p,[-1,3]),k=await j.array();r.dispose(p),r.dispose(j);let N=this.transformRawCoords(k,f,y,u),D=this.getBoxForHandLandmarks(N);this.storedBoxes[c]={...D,confidence:b};let X={landmarks:N,confidence:b,boxConfidence:d.confidence,fingerConfidence:b,box:{topLeft:D.startPoint,bottomRight:D.endPoint}};i.push(X)}else this.storedBoxes[c]=null;r.dispose(p)}else{let y=Z2(X2(d),rn),l={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:y.startPoint,bottomRight:y.endPoint},landmarks:[]};i.push(l)}}return this.storedBoxes=this.storedBoxes.filter(c=>c!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var an={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]},Ie,Ne,ln;async function m5(e,t){let n=await ln.estimateHands(e,t);if(!n)return[];let o=[];for(let s=0;s<n.length;s++){let A={};if(n[s].landmarks)for(let y of Object.keys(an))A[y]=an[y].map(l=>n[s].landmarks[l]);let a=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],c=[0,0,0,0];if(a&&a.length>0){for(let y of a)y[0]<i[0]&&(i[0]=y[0]),y[1]<i[1]&&(i[1]=y[1]),y[0]>i[2]&&(i[2]=y[0]),y[1]>i[3]&&(i[3]=y[1]);i[2]-=i[0],i[3]-=i[1],c=[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=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],c=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let d=G2(a);o.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:c,keypoints:a,annotations:A,landmarks:d})}return o}async function cn(e){var n,o;M.initial&&(Ie=null,Ne=null),!Ie||!Ne?[Ie,Ne]=await Promise.all([e.hand.enabled?L((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?L((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&h("cached model:",Ie.modelUrl),e.debug&&h("cached model:",Ne.modelUrl));let t=Ie?new q2(Ie):void 0;return t&&Ne&&(ln=new U2(t,Ne)),[Ie,Ne]}var r0=[null,null],Gs=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],he=[[0,0],[0,0]],Vs=["hand","fist","pinch","point","face","tip","pinchtip"],xn=4,yn=1.6,Zs=512,Xs=1.4,Y2=Number.MAX_SAFE_INTEGER,p5=0,te=[0,0],o0={boxes:[],hands:[]},fn={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 mn(e){var t;if(M.initial&&(r0[0]=null),r0[0])e.debug&&h("cached model:",r0[0].modelUrl);else{b2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),r0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=r0[0].executor?Object.values(r0[0].modelSignature.inputs):void 0;he[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[0]}async function pn(e){var t;if(M.initial&&(r0[1]=null),r0[1])e.debug&&h("cached model:",r0[1].modelUrl);else{r0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=r0[1].executor?Object.values(r0[1].modelSignature.inputs):void 0;he[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[1]}async function qs(e,t){let n=[];if(!e||!r0[0])return n;let o={},s=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Zs),a=Math.round(A*s/8)*8;o.resize=r.image.resizeBilinear(e,[A,a]),o.cast=r.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await r0[0].executeAsync(o.cast,Gs),o.boxes=r.squeeze(o.rawBoxes,[0,2]),o.scores=r.squeeze(o.rawScores,[0]);let i=r.unstack(o.scores,1);r.dispose(i[xn]),i.splice(xn,1),o.filtered=r.stack(i,1),r.dispose(i),o.max=r.max(o.filtered,1),o.argmax=r.argMax(o.filtered,1);let c=0;o.nms=await r.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await o.nms.data(),y=await o.max.data(),l=await o.argmax.data();for(let m of Array.from(d)){let x=r.slice(o.boxes,m,1),u=await x.data();r.dispose(x);let f=[u[1],u[0],u[3]-u[1],u[2]-u[0]],g=w2(f,Xs),P=[Math.trunc(f[0]*te[0]),Math.trunc(f[1]*te[1]),Math.trunc(f[2]*te[0]),Math.trunc(f[3]*te[1])],v=y[m],p=Vs[l[m]],b={id:c++,score:v,box:P,boxRaw:g,label:p};n.push(b)}return Object.keys(o).forEach(m=>r.dispose(o[m])),n.sort((m,x)=>x.score-m.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function u5(e,t,n){let o={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&&r0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=r.image.cropAndResize(e,[A],[0],[he[1][0],he[1][1]],"bilinear"),s.div=r.div(s.crop,O.tf255),[s.score,s.keypoints]=r0[1].execute(s.div,["Identity_1","Identity"]);let a=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(n.hand.minConfidence||0)){o.fingerScore=i,s.reshaped=r.reshape(s.keypoints,[-1,3]);let y=(await s.reshaped.array()).map(l=>[l[0]/he[1][1],l[1]/he[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);o.keypoints=y.map(l=>[te[0]*(l[0]+t.boxRaw[0]),te[1]*(l[1]+t.boxRaw[1]),l[2]||0]),o.landmarks=G2(o.keypoints);for(let l of Object.keys(fn))o.annotations[l]=fn[l].map(m=>o.landmarks&&o.keypoints[m]?o.keypoints[m]:null)}Object.keys(s).forEach(c=>r.dispose(s[c]))}return o}async function h5(e,t){var s,A;if(!((s=r0[0])!=null&&s.executor)||!((A=r0[1])!=null&&A.executor)||!r0[0].inputs[0].shape||!r0[1].inputs[0].shape)return[];te=[e.shape[2]||0,e.shape[1]||0],Y2++;let n=(t.hand.skipTime||0)>T()-p5,o=Y2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?o0.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>T()-p5,c=Y2<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(y=>u5(e,y,t))):t.skipAllowed&&i&&c&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(y=>u5(e,y,t))):(o0.boxes=await qs(e,t),p5=T(),o0.hands=await Promise.all(o0.boxes.map(y=>u5(e,y,t))),Y2=0);let d=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let y=0;y<o0.hands.length;y++){let l=f1(o0.hands[y].keypoints,te);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&o0.hands[y].fingerScore&&o0.hands[y].fingerScore>(t.hand.minConfidence||0)){let m=w2(l.box,yn),x=w2(l.boxRaw,yn);o0.boxes.push({...d[y],box:m,boxRaw:x})}}for(let y=0;y<o0.hands.length;y++){let l=_0(o0.hands[y].keypoints,te);o0.hands[y].box=l.box,o0.hands[y].boxRaw=l.boxRaw}a(o0.hands)})}var ne=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var A2={};re(A2,{connected:()=>J2,horizontal:()=>b5,kpt:()=>K2,relative:()=>T5,vertical:()=>g5});var K2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],b5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],g5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],T5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],J2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var S=ne(),v5=0;function hn(e,t){var a,i,c,d,y,l,m,x,u,f,g,P,v,p,b,j,k,N,D,X,W,V,J,U,q,f0;let n=T();if(!e)return ne();let o=Date.now()-e.timestamp,s=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(S.canvas=e.canvas),e.error&&(S.error=e.error),!S.body||e.body.length!==S.body.length)S.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let i0=e.body[R].box.map((C,F)=>((s-1)*S.body[R].box[F]+C)/s),b0=e.body[R].boxRaw.map((C,F)=>((s-1)*S.body[R].boxRaw[F]+C)/s),e0=e.body[R].keypoints.map((C,F)=>{var P0,t0,oe,Je,Oe,B5,H5,G5,V5;return{score:C.score,part:C.part,position:[S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].position[0]||0)+(C.position[0]||0))/s:C.position[0],S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].position[1]||0)+(C.position[1]||0))/s:C.position[1],S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].position[2]||0)+(C.position[2]||0))/s:C.position[2]],positionRaw:[S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].positionRaw[0]||0)+(C.positionRaw[0]||0))/s:C.positionRaw[0],S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].positionRaw[1]||0)+(C.positionRaw[1]||0))/s:C.positionRaw[1],S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].positionRaw[2]||0)+(C.positionRaw[2]||0))/s:C.positionRaw[2]],distance:[S.body[R].keypoints[F]?((s-1)*(((P0=S.body[R].keypoints[F].distance)==null?void 0:P0[0])||0)+(((t0=C.distance)==null?void 0:t0[0])||0))/s:(oe=C.distance)==null?void 0:oe[0],S.body[R].keypoints[F]?((s-1)*(((Je=S.body[R].keypoints[F].distance)==null?void 0:Je[1])||0)+(((Oe=C.distance)==null?void 0:Oe[1])||0))/s:(B5=C.distance)==null?void 0:B5[1],S.body[R].keypoints[F]?((s-1)*(((H5=S.body[R].keypoints[F].distance)==null?void 0:H5[2])||0)+(((G5=C.distance)==null?void 0:G5[2])||0))/s:(V5=C.distance)==null?void 0:V5[2]]}}),H={},z={connected:{}};(a=t.body.modelPath)!=null&&a.includes("efficientpose")?z=S2:(i=t.body.modelPath)!=null&&i.includes("blazepose")?z=P2:(c=t.body.modelPath)!=null&&c.includes("movenet")&&(z=A2);for(let[C,F]of Object.entries(z.connected)){let P0=[];for(let t0=0;t0<F.length-1;t0++){let oe=e0.find(Oe=>Oe.part===F[t0]),Je=e0.find(Oe=>Oe.part===F[t0+1]);oe&&Je&&P0.push([oe.position,Je.position])}H[C]=P0}S.body[R]={...e.body[R],box:i0,boxRaw:b0,keypoints:e0,annotations:H}}if(!S.hand||e.hand.length!==S.hand.length)S.hand=JSON.parse(JSON.stringify(e.hand));else for(let R=0;R<e.hand.length;R++){let i0=e.hand[R].box.map((z,C)=>((s-1)*S.hand[R].box[C]+z)/s),b0=e.hand[R].boxRaw.map((z,C)=>((s-1)*S.hand[R].boxRaw[C]+z)/s);S.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(S.hand[R].keypoints=e.hand[R].keypoints);let e0=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((z,C)=>z.map((F,P0)=>((s-1)*(S.hand[R].keypoints[C][P0]||1)+(F||0))/s)):[],H={};if(Object.keys(S.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)S.hand[R].annotations=e.hand[R].annotations,H=S.hand[R].annotations;else if(e.hand[R].annotations)for(let z of Object.keys(e.hand[R].annotations))H[z]=(l=(y=(d=e.hand[R])==null?void 0:d.annotations)==null?void 0:y[z])!=null&&l[0]?e.hand[R].annotations[z].map((C,F)=>C.map((P0,t0)=>((s-1)*S.hand[R].annotations[z][F][t0]+P0)/s)):null;S.hand[R]={...e.hand[R],box:i0,boxRaw:b0,keypoints:e0,annotations:H}}if(!S.face||e.face.length!==S.face.length)S.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let i0=e.face[R].box.map((H,z)=>((s-1)*S.face[R].box[z]+H)/s),b0=e.face[R].boxRaw.map((H,z)=>((s-1)*S.face[R].boxRaw[z]+H)/s),e0=e.face[R].annotations;if(Object.keys(S.face[R].annotations).length!==Object.keys(e.face[R].annotations).length)S.face[R].annotations=e.face[R].annotations,e0=S.face[R].annotations;else if(e.face[R].annotations)for(let H of Object.keys(e.face[R].annotations))e0[H]=(u=(x=(m=e.face[R])==null?void 0:m.annotations)==null?void 0:x[H])!=null&&u[0]?e.face[R].annotations[H].map((z,C)=>z.map((F,P0)=>((s-1)*S.face[R].annotations[H][C][P0]+F)/s)):null;if(e.face[R].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(f=e.face[R].rotation)==null?void 0:f.matrix,H.angle={roll:((s-1)*(((P=(g=S.face[R].rotation)==null?void 0:g.angle)==null?void 0:P.roll)||0)+(((p=(v=e.face[R].rotation)==null?void 0:v.angle)==null?void 0:p.roll)||0))/s,yaw:((s-1)*(((j=(b=S.face[R].rotation)==null?void 0:b.angle)==null?void 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A=e[0][s],a=Math.round(100*A[51+4])/100;if(a>t.body.minConfidence){let i=[];for(let l=0;l<17;l++){let m=A[3*l+2];if(m>t.body.minConfidence){let x=[A[3*l+1],A[3*l+0]];i.push({part:K2[l],score:Math.round(100*m)/100,positionRaw:x,position:[Math.round((n.shape[2]||0)*x[0]),Math.round((n.shape[1]||0)*x[1])]})}}let c=_0(i.map(l=>l.position),[n.shape[2],n.shape[1]]),d={};for(let[l,m]of Object.entries(J2)){let x=[];for(let u=0;u<m.length-1;u++){let f=i.find(P=>P.part===m[u]),g=i.find(P=>P.part===m[u+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&x.push([f.position,g.position])}d[l]=x}let y={id:s,score:a,box:c.box,boxRaw:c.boxRaw,keypoints:[...i],annotations:d};k5(y),o.push(y)}}return o.sort((s,A)=>A.score-s.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function E5(e,t){var s;if(!(A0!=null&&A0.executor)||!((s=A0==null?void 0:A0.inputs)!=null&&s[0].shape))return[];t.skipAllowed||(Le.boxes.length=0),w5++;let n=(t.body.skipTime||0)>T()-Le.last,o=w5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?Le.bodies:new Promise(async A=>{let a={};w5=0,a.input=Mn(e,Q2),a.res=A0==null?void 0:A0.execute(a.input),Le.last=T();let i=await a.res.array();Le.bodies=a.res.shape[2]===17?Qs(i,t,e):_s(i,t,e);for(let c of Le.bodies)Pn(c,[e.shape[2]||1,e.shape[1]||1]),Rn(c.keypoints);Object.keys(a).forEach(c=>r.dispose(a[c])),A(Le.bodies)})}var H0,_2=[],En=0,z5=Number.MAX_SAFE_INTEGER,et=0,$2=2.5;async function zn(e){if(!H0||M.initial){H0=await L(e.object.modelPath);let t=H0!=null&&H0.executor?Object.values(H0.modelSignature.inputs):void 0;et=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&h("cached model:",H0.modelUrl);return H0}async function $s(e,t,n){var d,y;let o=0,s=[],A=et;for(let l of[1,2,4]){let m=l*13,x=r.squeeze(e.find(p=>p.shape[1]===m**2&&(p.shape[2]||0)===De.length)),u=await x.array(),f=r.squeeze(e.find(p=>p.shape[1]===m**2&&(p.shape[2]||0)<De.length)),g=r.reshape(f,[-1,4,(((d=f.shape)==null?void 0:d[1])||0)/4]),P=r.argMax(g,2),v=await P.array();for(let p=0;p<x.shape[0];p++)for(let b=0;b<(((y=x.shape)==null?void 0:y[1])||0);b++){let j=u[p][b];if(j>(n.object.minConfidence||0)&&b!==61){let k=(.5+Math.trunc(p%m))/m,N=(.5+Math.trunc(p/m))/m,D=v[p].map(R=>R*(m/l/A)),[X,W]=[k-$2/l*D[0],N-$2/l*D[1]],[V,J]=[k+$2/l*D[2]-X,N+$2/l*D[3]-W],U=[X,W,V,J];U=U.map(R=>Math.max(0,Math.min(R,1)));let q=[U[0]*t[0],U[1]*t[1],U[2]*t[0],U[3]*t[1]],f0={id:o++,score:Math.round(100*j)/100,class:b+1,label:De[b].label,box:q.map(R=>Math.trunc(R)),boxRaw:U};s.push(f0)}}r.dispose([x,f,g,P])}let a=s.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=s.map(l=>l.score),c=[];if(a&&a.length>0){let l=await r.image.nonMaxSuppressionAsync(a,i,n.object.maxDetected||0,n.object.iouThreshold,n.object.minConfidence);c=Array.from(await l.data()),r.dispose(l)}return 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i2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],eA=i2.length,a2=i2.reduce((e,t,n)=>(e[t]=n,e),{}),tA=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],K7=tA.map(([e,t])=>[a2[e],a2[t]]),jn=[["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 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?W1(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?i3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((s=this.instance.config.face.liveness)!=null&&s.enabled)&&!this.models.liveness?x3(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((A=this.instance.config.face.description)!=null&&A.enabled)&&!this.models.faceres?o3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((a=this.instance.config.face.emotion)!=null&&a.enabled)&&!this.models.emotion?$1(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((i=this.instance.config.face.iris)!=null&&i.enabled)&&!((c=this.instance.config.face.attention)!=null&&c.enabled)&&!this.models.iris?V1(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((d=this.instance.config.face.mesh)!=null&&d.enabled)&&!this.models.facemesh?Y1(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((y=this.instance.config.face.gear)!=null&&y.enabled)&&!this.models.gear?p3(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((l=this.instance.config.face.ssrnet)!=null&&l.enabled)&&!this.models.ssrnetage?g3(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((m=this.instance.config.face.ssrnet)!=null&&m.enabled)&&!this.models.ssrnetgender?M3(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((x=this.instance.config.face.mobilefacenet)!=null&&x.enabled)&&!this.models.mobilefacenet?z3(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((u=this.instance.config.face.insightface)!=null&&u.enabled)&&!this.models.insightface?L3(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((f=this.instance.config.body.modelPath)!=null&&f.includes("blazepose"))?b1(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?h1(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((g=this.instance.config.body.modelPath)!=null&&g.includes("efficientpose"))?P1(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((P=this.instance.config.body.modelPath)!=null&&P.includes("movenet"))?kn(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((v=this.instance.config.body.modelPath)!=null&&v.includes("posenet"))?Wn(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((b=(p=this.instance.config.hand.detector)==null?void 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bt=M.perfadd?bt+Math.round(T()-o):Math.round(T()-o),t.performance.draw=bt,A}function gt(){n0.faceLabels=ae.face,n0.bodyLabels=ae.body,n0.bodyPartLabels=ae.bodyPart,n0.handLabels=ae.hand,n0.fingerLabels=ae.finger,n0.objectLabels=ae.object,n0.gestureLabels=ae.gesture}var M2={};re(M2,{connected:()=>Rt,kpt:()=>vt});var vt=["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"],Rt={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 C0,ke=224,c1,_r=5,P2=[8,16,32,32,32];function $r(){let e=[],t=0;for(;t<_r;){let n=0,o=t;for(;o<P2.length&&P2[o]===P2[t];)n+=2,o++;let s=P2[t],A=Math.ceil(ke/s),a=Math.ceil(ke/s);for(let i=0;i<A;++i)for(let c=0;c<a;++c)for(let d=0;d<n;++d)e.push({x:(c+.5)/a,y:(i+.5)/A});t=o}c1={x:r.tensor1d(e.map(n=>n.x)),y:r.tensor1d(e.map(n=>n.y))}}async function d1(e){if(M.initial&&(C0=null),!C0&&e.body.detector&&e.body.detector.modelPath){C0=await N(e.body.detector.modelPath);let t=C0!=null&&C0.executor?Object.values(C0.modelSignature.inputs):void 0;ke=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&C0&&h("cached model:",C0.modelUrl);return $r(),C0}var l1=[5,5];function es(e,t){return r.tidy(()=>{let n=r.split(e,12,1),o=r.squeeze(n[0]),s=r.squeeze(n[1]),A=r.squeeze(n[2]),a=r.squeeze(n[3]);o=r.add(r.div(o,ke),t.x),s=r.add(r.div(s,ke),t.y),A=r.mul(r.div(A,ke),l1[0]),a=r.mul(r.div(a,ke),l1[1]);let i=r.sub(o,r.div(A,2)),c=r.sub(s,r.div(a,2)),d=r.add(i,A),y=r.add(c,a);return r.stack([i,c,d,y],1)})}async function ts(e,t,n,o){var d,y;let s=[],A={};A.boxes=es(e,c1),A.scores=r.sigmoid(t),A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.scores,1,((d=n.body.detector)==null?void 0:d.minConfidence)||.1,((y=n.body.detector)==null?void 0:y.iouThreshold)||.1);let a=await A.nms.data(),i=await A.scores.data(),c=await A.boxes.array();for(let l of Array.from(a)){let m=i[l],x=c[l],p=[Math.round(x[0]*o[0]),Math.round(x[1]*o[1]),Math.round(x[2]*o[0]),Math.round(x[3]*o[1])],f={score:m,boxRaw:x,box:p};s.push(f)}return Object.keys(A).forEach(l=>r.dispose(A[l])),s}async function x1(e,t,n){let o={};o.res=C0==null?void 0:C0.execute(e,["Identity"]),o.logitsRaw=r.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=r.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=r.squeeze(o.logitsRaw),o.boxes=r.squeeze(o.boxesRaw);let s=await ts(o.boxes,o.logits,t,n);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}function _0(e,t=[1,1]){let 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Object.keys(o).forEach(i=>r.dispose(o[i])),s}function os(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ie[2][0]+ie[2][1])/t[0]-ie[2][0]),Math.trunc(o.position[1]*(t[1]+ie[1][0]+ie[1][1])/t[1]-ie[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(n){let o=n[2]-n[0],s=n[3]-n[1];for(let A of e)A.positionRaw=[A.positionRaw[0]/s+n[1],A.positionRaw[1]/o+n[0],A.positionRaw[2]],A.position=[Math.trunc(A.positionRaw[0]*t[0]),Math.trunc(A.positionRaw[1]*t[1]),A.positionRaw[2]]}return e}function rs(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),o=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let s=e.find(i=>i.part==="rightPalm"),A=e.find(i=>i.part==="rightWrist"),a=e.find(i=>i.part==="rightIndex");s.position[2]=((A.position[2]||0)+(a.position[2]||0))/2}async function ss(e,t,n){if(!(R0!=null&&R0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=R0==null?void 0:R0.execute(e,ns.landmarks);let s=(await o.poseflag.data())[0],A=await o.ld.data(),a=await o.world.data();Object.keys(o).forEach(p=>r.dispose(o[p]));let i=[],c=5;for(let p=0;p<A.length/c;p++){let f=m1(A[c*p+3]),g=m1(A[c*p+4]),P=Math.trunc(100*f*g*s)/100,v=[A[c*p+0]/Pt,A[c*p+1]/Pt,A[c*p+2]+0],u=[Math.trunc(n[0]*v[0]),Math.trunc(n[1]*v[1]),v[2]],b=[a[c*p+0],a[c*p+1],a[c*p+2]+0];i.push({part:vt[p],positionRaw:v,position:u,distance:b,score:P})}if(s<(t.body.minConfidence||0))return null;rs(i);let d=os(i,n),y=d.map(p=>p.position),l=_0(y,[n[0],n[1]]),m={};for(let[p,f]of Object.entries(Rt)){let g=[];for(let P=0;P<f.length-1;P++){let v=d.find(b=>b.part===f[P]),u=d.find(b=>b.part===f[P+1]);v&&u&&g.push([v.position,u.position])}m[p]=g}return{id:0,score:Math.trunc(100*s)/100,box:l.box,boxRaw:l.boxRaw,keypoints:d,annotations:m}}async function kt(e,t){var A,a,i;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>T()-f1,s=Mt<(t.body.skipFrames||0);if(t.skipAllowed&&o&&s&&E2!==null)Mt++;else{let c=[];if((a=(A=t.body)==null?void 0:A.detector)!=null&&a.enabled){let d=p1(e,224);c=await x1(d,t,n),r.dispose(d)}else c=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let d=0;d<c.length;d++){let y=p1(e,256,(i=c[d])==null?void 0:i.boxRaw);E2.length=0;let l=await ss(y,t,n);r.dispose(y),l&&(l.id=d,E2.push(l))}f1=T(),Mt=0}return E2}var De=[{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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St=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],jt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var _,R1=0,T0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},It=Number.MAX_SAFE_INTEGER;async function M1(e){return M.initial&&(_=null),_?e.debug&&h("cached model:",_.modelUrl):_=await N(e.body.modelPath),_}async function as(e,t){let[n,o]=e.shape,s=r.reshape(e,[o*n]),A=r.max(s,0),a=(await A.data())[0];if(a>t){let i=r.argMax(s,0),c=r.mod(i,n),d=(await c.data())[0],y=r.div(i,n),l=(await y.data())[0];return r.dispose([s,A,i,c,y]),[d,l,a]}return r.dispose([s,A]),[0,0,a]}async function 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t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let s=t.strides[o],A=Math.floor((e+s-1)/s),a=Math.floor((e+s-1)/s),i=t.anchors[o];for(let c=0;c<A;c++){let d=s*(c+.5);for(let y=0;y<a;y++){let l=s*(y+.5);for(let m=0;m<i;m++)n.push([l,d])}}}return n}function I1(e,t,n,o,s){let A=Fe(t),a=e.map(x=>[A[0]/s*(x[0]-s/2),A[1]/s*(x[1]-s/2),x[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,c=i?S1(n,[0,0]):Ot,d=i?a.map(x=>[...xs(x,c),x[2]]):a,y=i?ds(o):Ot,l=S2(t),m=[Ee(l,y[0]),Ee(l,y[1])];return d.map(x=>[Math.trunc(x[0]+m[0]),Math.trunc(x[1]+m[1]),Math.trunc(x[2]||0)])}function N1(e,t,n,o){let s=t.landmarks.length>=pt.count?pt.symmetryLine:ve.symmetryLine,A=0,a=Ot,i;if(e&&M.kernels.includes("rotatewithoffset"))if(A=ls(t.landmarks[s[0]],t.landmarks[s[1]]),A&&A!==0&&Math.abs(A)>.2){let d=S2(t),y=[d[0]/n.shape[2],d[1]/n.shape[1]],l=r.image.rotateWithOffset(n,A,0,[y[0],y[1]]);a=S1(-A,d),i=Lt(t,l,[o,o]),r.dispose(l)}else i=Lt(t,n,[o,o]);else i=Lt(t,n,[o,o]);return[A,a,i]}var ys=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},L1=(e,t)=>{let n=ys(e),o=Fe(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var O1=6,fs=1.4,G0,O2=null,le=0,Be=null,C1=()=>le;async function W1(e){var t;return M.initial&&(G0=null),G0?e.debug&&h("cached model:",G0.modelUrl):G0=await N((t=e.face.detector)==null?void 0:t.modelPath),le=G0.executor&&G0.inputs[0].shape?G0.inputs[0].shape[2]:256,Be=r.scalar(le,"int32"),O2=r.tensor2d(j1(le)),G0}function ms(e){if(!O2||!Be)return r.zeros([0,0]);let t={};t.boxStarts=r.slice(e,[0,1],[-1,2]),t.centers=r.add(t.boxStarts,O2),t.boxSizes=r.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=r.div(t.boxSizes,Be),t.centersNormalized=r.div(t.centers,Be),t.halfBoxSize=r.div(t.boxSizesNormalized,O.tf2),t.starts=r.sub(t.centersNormalized,t.halfBoxSize),t.ends=r.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=r.mul(t.starts,Be),t.endNormalized=r.mul(t.ends,Be);let n=r.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>r.dispose(t[o])),n}async function D1(e,t){var i,c,d,y,l,m;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=r.image.resizeBilinear(e,[le,le]),n.div=r.div(n.resized,O.tf127),n.normalized=r.sub(n.div,O.tf05);let o=G0==null?void 0:G0.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let x=o.sort((p,f)=>p.size-f.size);n.concat384=r.concat([x[0],x[2]],2),n.concat512=r.concat([x[1],x[3]],2),n.concat=r.concat([n.concat512,n.concat384],1),n.batch=r.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=r.squeeze(o[0]):n.batch=r.squeeze(o);r.dispose(o),n.boxes=ms(n.batch),n.logits=r.slice(n.batch,[0,0],[-1,1]),n.sigmoid=r.sigmoid(n.logits),n.scores=r.squeeze(n.sigmoid),n.nms=await r.image.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((c=t.face.detector)==null?void 0:c.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0);let s=await n.nms.array(),A=[],a=await n.scores.data();for(let x=0;x<s.length;x++){let p=a[s[x]];if(p>(((y=t.face.detector)==null?void 0:y.minConfidence)||0)){let f={};f.bbox=r.slice(n.boxes,[s[x],0],[1,-1]),f.slice=r.slice(n.batch,[s[x],O1-1],[1,-1]),f.squeeze=r.squeeze(f.slice),f.landmarks=r.reshape(f.squeeze,[O1,-1]);let g=await f.bbox.data(),P={startPoint:[g[0],g[1]],endPoint:[g[2],g[3]],landmarks:await f.landmarks.array(),confidence:p};f.anchor=r.slice(O2,[s[x],0],[1,2]);let v=await f.anchor.data(),u=E1(P,[(e.shape[2]||0)/le,(e.shape[1]||0)/le],v),b=N2(u,t.face.scale||fs),j=L2(b);j.size[0]>(((l=t.face.detector)==null?void 0:l.minSize)||0)&&j.size[1]>(((m=t.face.detector)==null?void 0:m.minSize)||0)&&A.push(j),Object.keys(f).forEach(k=>r.dispose(f[k]))}}return Object.keys(n).forEach(x=>r.dispose(n[x])),A}var w0,ce=0,ps=2.3,Wt=D0.leftEyeLower0,Dt=D0.rightEyeLower0,He={leftBounds:[Wt[0],Wt[Wt.length-1]],rightBounds:[Dt[0],Dt[Dt.length-1]]},Ge={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function V1(e){var t,n;return M.initial&&(w0=null),w0?e.debug&&h("cached model:",w0.modelUrl):w0=await N((t=e.face.iris)==null?void 0:t.modelPath),ce=w0!=null&&w0.executor&&((n=w0.inputs)!=null&&n[0].shape)?w0.inputs[0].shape[2]:0,ce===-1&&(ce=64),w0}function C2(e,t,n,o){for(let s=0;s<ut.length;s++){let{key:A,indices:a}=ut[s],i=D0[`${n}${A}`];if(!o||o.includes(A))for(let c=0;c<a.length;c++){let d=a[c];e[i[c]]=[t[d][0],t[d][1],(t[d][2]+e[i[c]][2])/2]}}}var us=e=>{let t=e[He.leftBounds[0]][2],n=e[He.rightBounds[0]][2];return t-n},B1=(e,t,n,o,s,A=!1)=>{let a=L2(N2(z1([e[n],e[o]]),ps)),i=Fe(a),c=r.image.cropAndResize(t,[[a.startPoint[1]/s,a.startPoint[0]/s,a.endPoint[1]/s,a.endPoint[0]/s]],[0],[ce,ce]);if(A&&M.kernels.includes("flipleftright")){let d=r.image.flipLeftRight(c);r.dispose(c),c=d}return{box:a,boxSize:i,crop:c}},H1=(e,t,n,o=!1)=>{let s=[];for(let A=0;A<Ge.numCoordinates;A++){let a=e[A*3],i=e[A*3+1],c=e[A*3+2];s.push([(o?1-a/ce:a/ce)*n[0]+t.startPoint[0],i/ce*n[1]+t.startPoint[1],c])}return{rawCoords:s,iris:s.slice(Ge.index)}},G1=(e,t,n)=>{let o=e[D0[`${n}EyeUpper0`][Ge.upperCenter]][2],s=e[D0[`${n}EyeLower0`][Ge.lowerCenter]][2],A=(o+s)/2;return t.map((a,i)=>{let c=A;return i===2?c=o:i===4&&(c=s),[a[0],a[1],c]})};async function Z1(e,t,n){if(!(w0!=null&&w0.executor))return e;let{box:o,boxSize:s,crop:A}=B1(e,t,He.leftBounds[0],He.leftBounds[1],n,!0),{box:a,boxSize:i,crop:c}=B1(e,t,He.rightBounds[0],He.rightBounds[1],n,!0),d=r.concat([A,c]);r.dispose(A),r.dispose(c);let y=w0.execute(d);r.dispose(d);let l=await y.data();r.dispose(y);let m=l.slice(0,Ge.numCoordinates*3),{rawCoords:x,iris:p}=H1(m,o,s,!0),f=l.slice(Ge.numCoordinates*3),{rawCoords:g,iris:P}=H1(f,a,i,!1),v=us(e);Math.abs(v)<30?(C2(e,x,"left",null),C2(e,g,"right",null)):v<1?C2(e,x,"left",["EyeUpper0","EyeLower0"]):C2(e,g,"right",["EyeUpper0","EyeLower0"]);let u=G1(e,p,"left"),b=G1(e,P,"right");return e.concat(u).concat(b)}async function q1(e,t){var A,a,i,c,d,y,l,m,x,p;let n={lips:await((a=(A=t.filter(f=>f.size===160))==null?void 0:A[0])==null?void 0:a.data()),irisL:await((c=(i=t.filter(f=>f.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((y=(d=t.filter(f=>f.size===142))==null?void 0:d[0])==null?void 0:y.data()),irisR:await((m=(l=t.filter(f=>f.size===10))==null?void 0:l[1])==null?void 0:m.data()),eyeR:await((p=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:p.data())};for(let f of Object.values(n))if(!f)return e;let o=Me.reduce((f,g)=>f+=e[g][2],0)/Me.length;for(let f=0;f<n.irisL.length/2;f++)e.push([n.irisL[2*f+0],n.irisL[2*f+1],o]);let s=Pe.reduce((f,g)=>f+=e[g][2],0)/Pe.length;for(let f=0;f<n.irisR.length/2;f++)e.push([n.irisR[2*f+0],n.irisR[2*f+1],s]);for(let f=0;f<n.eyeL.length/2;f++)e[Me[f]]=[n.eyeL[2*f+0],n.eyeL[2*f+1],e[Me[f]][2]];for(let f=0;f<n.eyeR.length/2;f++)e[Pe[f]]=[n.eyeR[2*f+0],n.eyeR[2*f+1],e[Pe[f]][2]];for(let f=0;f<n.lips.length/2;f++)e[n2[f]]=[n.lips[2*f+0],n.lips[2*f+1],e[n2[f]][2]];return e}var K0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},K=null,o2=0;async function U1(e,t){var c,d,y,l,m,x,p,f,g,P;let n=(((c=t.face.detector)==null?void 0:c.skipTime)||0)>T()-K0.timestamp,o=K0.skipped<(((d=t.face.detector)==null?void 0:d.skipFrames)||0);!t.skipAllowed||!n||!o||K0.boxes.length===0?(K0.boxes=await D1(e,t),K0.timestamp=T(),K0.skipped=0):K0.skipped++;let s=[],A=[],a=0,i=o2;for(let v=0;v<K0.boxes.length;v++){let u=K0.boxes[v],b=0,j,k={id:a++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([b,j,k.tensor]=N1((y=t.face.detector)==null?void 0:y.rotation,u,e,(l=t.face.mesh)!=null&&l.enabled?o2:C1()),t.filter.equalization){let L=k.tensor?await d2(k.tensor):void 0;r.dispose(k.tensor),L&&(k.tensor=L)}if(k.boxScore=Math.round(100*u.confidence)/100,!((m=t.face.mesh)!=null&&m.enabled)||!(K!=null&&K.executor)){k.box=j2(u,e),k.boxRaw=I2(u,e),k.score=k.boxScore,k.size=u.size,k.mesh=u.landmarks,k.meshRaw=k.mesh.map(L=>[L[0]/(e.shape[2]||0),L[1]/(e.shape[1]||0),(L[2]||0)/i]);for(let L of Object.keys(ve))k.annotations[L]=[k.mesh[ve[L]]]}else if(!K)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((x=t.face.attention)!=null&&x.enabled&&!M.kernels.includes("atan2"))return t.face.attention.enabled=!1,r.dispose(k.tensor),s;let L=K.execute(k.tensor),X=await L.find(D=>D.shape[D.shape.length-1]===1).data();if(k.faceScore=Math.round(100*X[0])/100,k.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1)){if(u.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=j2(u,e),k.boxRaw=I2(u,e),k.score=k.boxScore,k.mesh=u.landmarks,k.meshRaw=k.mesh.map(D=>[D[0]/(e.shape[2]||1),D[1]/(e.shape[1]||1),(D[2]||0)/i]);for(let D of Object.keys(ve))k.annotations[D]=[k.mesh[ve[D]]]}}else{let D=L.find(q=>q.shape[q.shape.length-1]===1404),V=r.reshape(D,[-1,3]),J=await V.array();r.dispose(V),(f=t.face.attention)!=null&&f.enabled?J=await q1(J,L):(g=t.face.iris)!=null&&g.enabled&&(J=await Z1(J,k.tensor,o2)),k.mesh=I1(J,u,b,j,o2),k.meshRaw=k.mesh.map(q=>[q[0]/(e.shape[2]||0),q[1]/(e.shape[1]||0),(q[2]||0)/i]);for(let q of Object.keys(D0))k.annotations[q]=D0[q].map(f0=>k.mesh[f0]);k.score=k.faceScore;let U={...L1(k.mesh,u),confidence:u.confidence,landmarks:u.landmarks,size:u.size};k.box=j2(U,e),k.boxRaw=I2(U,e),A.push(U)}r.dispose(L)}k.score>(((P=t.face.detector)==null?void 0:P.minConfidence)||1)?s.push(k):r.dispose(k.tensor)}return K0.boxes=A,s}async function Y1(e){var t,n,o,s,A,a;return M.initial&&(K=null),(t=e.face.attention)!=null&&t.enabled&&(K!=null&&K.signature)&&Object.keys(((n=K==null?void 0:K.signature)==null?void 0:n.outputs)||{}).length<6&&(K=null),K?e.debug&&h("cached model:",K.modelUrl):(o=e.face.attention)!=null&&o.enabled?K=await N(e.face.attention.modelPath):K=await N((s=e.face.mesh)==null?void 0:s.modelPath),o2=K.executor&&((A=K==null?void 0:K.inputs)!=null&&A[0].shape)?(a=K==null?void 0:K.inputs)==null?void 0:a[0].shape[2]:256,K}var K1=Re,J1=t2;var Ht=[],d0,W2=[],Q1=0,_1=0,Bt=Number.MAX_SAFE_INTEGER,Gt=!1;async function $1(e){var t,n,o;return M.initial&&(d0=null),d0?e.debug&&h("cached model:",d0.modelUrl):(d0=await N((t=e.face.emotion)==null?void 0:t.modelPath),Gt=((o=(n=d0==null?void 0:d0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,Gt?Ht=["angry","disgust","fear","happy","neutral","sad","surprise"]:Ht=["angry","disgust","fear","happy","sad","surprise","neutral"]),d0}async function Vt(e,t,n,o){var a,i;if(!d0)return[];let s=Bt<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),A=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>T()-_1;return t.skipAllowed&&A&&s&&Q1===o&&W2[n]&&W2[n].length>0?(Bt++,W2[n]):(Bt=0,new Promise(async c=>{var y;let d=[];if((y=t.face.emotion)!=null&&y.enabled){let l={},m=d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[2]:0;if(t.face.emotion.crop>0){let p=t.face.emotion.crop,f=[[p,p,1-p,1-p]];l.resize=r.image.cropAndResize(e,f,[0],[m,m])}else l.resize=r.image.resizeBilinear(e,[m,m],!1);Gt?(l.mul=r.mul(l.resize,255),l.normalize=r.sub(l.mul,[103.939,116.779,123.68]),l.emotion=d0==null?void 0:d0.execute(l.normalize)):(l.channels=r.mul(l.resize,O.rgb),l.grayscale=r.sum(l.channels,3,!0),l.grayscaleSub=r.sub(l.grayscale,O.tf05),l.grayscaleMul=r.mul(l.grayscaleSub,O.tf2),l.emotion=d0==null?void 0:d0.execute(l.grayscaleMul)),_1=T();let x=await l.emotion.data();for(let p=0;p<x.length;p++)x[p]>(t.face.emotion.minConfidence||0)&&d.push({score:Math.min(.99,Math.trunc(100*x[p])/100),emotion:Ht[p]});d.sort((p,f)=>f.score-p.score),Object.keys(l).forEach(p=>r.dispose(l[p]))}W2[n]=d,Q1=o,c(d)}))}var p0,de=[],t3=0,n3=0,Zt=Number.MAX_SAFE_INTEGER;async function o3(e){var t;return M.initial&&(p0=null),p0?e.debug&&h("cached model:",p0.modelUrl):p0=await N((t=e.face.description)==null?void 0:t.modelPath),p0}function bs(e){let t=e.image||e.tensor||e;if(!(p0!=null&&p0.inputs[0].shape))return t;let n=r.image.resizeBilinear(t,[p0.inputs[0].shape[2],p0.inputs[0].shape[1]],!1),o=r.mul(n,O.tf255);return r.dispose(n),o}async function Xt(e,t,n,o){var i,c,d,y;let s={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(p0!=null&&p0.executor))return s;let A=Zt<(((i=t.face.description)==null?void 0:i.skipFrames)||0),a=(((c=t.face.description)==null?void 0:c.skipTime)||0)>T()-t3;return t.skipAllowed&&A&&a&&n3===o&&((d=de==null?void 0:de[n])==null?void 0:d.age)>0&&((y=de==null?void 0:de[n])==null?void 0:y.genderScore)>0?(Zt++,de[n]):(Zt=0,new Promise(async l=>{var m;if((m=t.face.description)!=null&&m.enabled){let x=bs(e),p=p0==null?void 0:p0.execute(x);t3=T(),r.dispose(x);let g=await p.find(W=>W.shape[1]===1).data(),P=Math.trunc(200*Math.abs(g[0]-.5))/100;P>(t.face.description.minConfidence||0)&&(s.gender=g[0]<=.5?"female":"male",s.genderScore=Math.min(.99,P));let v=r.argMax(p.find(W=>W.shape[1]===100),1),u=(await v.data())[0];r.dispose(v);let j=await p.find(W=>W.shape[1]===100).data();s.age=Math.round(j[u-1]>j[u+1]?10*u-100*j[u-1]:10*u+100*j[u+1])/10,(Number.isNaN(g[0])||Number.isNaN(j[0]))&&h("faceres error:",{model:p0,result:p});let 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z0,s5=[],k3=0,w3=0,E3=Number.MAX_SAFE_INTEGER;async function z3(e){var t;return M.initial&&(z0=null),z0?e.debug&&h("cached model:",z0.modelUrl):z0=await N((t=e.face.mobilefacenet)==null?void 0:t.modelPath),z0}async function A5(e,t,n,o){var a,i;if(!(z0!=null&&z0.executor))return[];let s=E3<(((a=t.face.mobilefacenet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.mobilefacenet)==null?void 0:i.skipTime)||0)>T()-w3;return t.skipAllowed&&A&&s&&k3===o&&s5[n]?(E3++,s5[n]):new Promise(async c=>{var y;let d=[];if((y=t.face.mobilefacenet)!=null&&y.enabled&&(z0!=null&&z0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[z0.inputs[0].shape[2],z0.inputs[0].shape[1]],!1),l.data=z0.execute(l.crop);let m=await l.data.data();d=Array.from(m),Object.keys(l).forEach(x=>r.dispose(l[x]))}s5[n]=d,k3=o,w3=T(),c(d)})}var S0,a5=[],j3=0,I3=0,N3=Number.MAX_SAFE_INTEGER;async function L3(e){return M.initial&&(S0=null),S0?e.debug&&h("cached model:",S0.modelUrl):S0=await 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V2(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function r2(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function _3(e,t,n){let o=t.shape[1],s=t.shape[2],A=[[e.startPoint[1]/o,e.startPoint[0]/s,e.endPoint[1]/o,e.endPoint[0]/s]];return r.image.cropAndResize(t,A,[0],n)}function $3(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.palmLandmarks.map(A=>[A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:s,confidence:e.confidence}}function Z2(e,t=1.5){let n=r2(e),o=V2(e),s=[t*o[0]/2,t*o[1]/2],A=[n[0]-s[0],n[1]-s[1]],a=[n[0]+s[0],n[1]+s[1]];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function X2(e){let t=r2(e),n=V2(e),s=Math.max(...n)/2,A=[t[0]-s,t[1]-s],a=[t[0]+s,t[1]+s];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function Ls(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function en(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Ls(n)}var J3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ue(e,t){let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}function Os(e,t){let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n}function Q3(e,t){let n=[],o=e.length;for(let s=0;s<o;s++){n.push([]);for(let A=0;A<o;A++)n[s].push(ue(e[s],Os(t,A)))}return n}function x5(e,t){let n=Math.cos(e),o=Math.sin(e),s=[[n,-o,0],[o,n,0],[0,0,1]],A=J3(t[0],t[1]),a=Q3(A,s),i=J3(-t[0],-t[1]);return Q3(a,i)}function tn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-ue(t[0],n),-ue(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]}function y5(e,t){return[ue(e,t[0]),ue(e,t[1])]}var 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o=r.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(s=>r.dispose(n[s])),o}normalizeLandmarks(t,n){let o={};o.reshape=r.reshape(t,[-1,7,2]),o.div=r.div(o.reshape,this.inputSizeTensor),o.landmarks=r.add(o.div,this.anchors[n]?this.anchors[n]:0);let s=r.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}async predict(t,n){var i;let o={};o.resize=r.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=r.div(o.resize,O.tf127),o.image=r.sub(o.div,O.tf1),o.batched=this.model.execute(o.image),o.predictions=r.squeeze(o.batched),o.slice=r.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=r.sigmoid(o.slice),o.scores=r.squeeze(o.sigmoid);let s=await o.scores.data();o.boxes=r.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await r.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((i=n.hand)==null?void 0:i.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let A=await o.nms.array(),a=[];for(let c of A){let d={};d.box=r.slice(o.norm,[c,0],[1,-1]),d.slice=r.slice(o.predictions,[c,5],[1,14]),d.norm=this.normalizeLandmarks(d.slice,c),d.palmLandmarks=r.reshape(d.norm,[-1,2]);let y=await d.box.data(),l=y.slice(0,2),m=y.slice(2,4),x=await d.palmLandmarks.array(),p={startPoint:l,endPoint:m,palmLandmarks:x,confidence:s[c]},f=$3(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);a.push(f),Object.keys(d).forEach(g=>r.dispose(d[g]))}return Object.keys(o).forEach(c=>r.dispose(o[c])),a}};var Ds=5,rn=1.65,sn=[0,5,9,13,17,1,2],Fs=0,Bs=2,An=0,U2=class{constructor(t,n){E(this,"handDetector");E(this,"handPoseModel");E(this,"inputSize");E(this,"storedBoxes");E(this,"skipped");E(this,"detectedHands");var o,s,A;this.handDetector=t,this.handPoseModel=n,this.inputSize=((A=(s=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:s[0].shape)==null?void 0:A[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(a=>a[0]),o=t.map(a=>a[1]),s=[Math.min(...n),Math.min(...o)],A=[Math.max(...n),Math.max(...o)];return{startPoint:s,endPoint:A}}getBoxForPalmLandmarks(t,n){let o=t.map(A=>y5([...A,1],n)),s=this.calculateLandmarksBoundingBox(o);return Z2(X2(s),Ds)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Z2(X2(n),rn);o.palmLandmarks=[];for(let s=0;s<sn.length;s++)o.palmLandmarks.push(t[sn[s]].slice(0,2));return o}transformRawCoords(t,n,o,s){let A=V2(n),a=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],i=t.map(x=>[a[0]*(x[0]-this.inputSize/2),a[1]*(x[1]-this.inputSize/2),a[2]*x[2]]),c=x5(o,[0,0]),d=i.map(x=>[...y5(x,c),x[2]]),y=tn(s),l=[...r2(n),1],m=[ue(l,y[0]),ue(l,y[1])];return d.map(x=>[Math.trunc(x[0]+m[0]),Math.trunc(x[1]+m[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,s,A=(n.hand.skipTime||0)>T()-An,a=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&A&&a&&(s=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(o=!0));let i=[];for(let c=0;c<this.storedBoxes.length;c++){let d=this.storedBoxes[c];if(d)if(n.hand.landmarks){let y=n.hand.rotation?en(d.palmLandmarks[Fs],d.palmLandmarks[Bs]):0,l=r2(d),m=[l[0]/t.shape[2],l[1]/t.shape[1]],x=n.hand.rotation&&M.kernels.includes("rotatewithoffset")?r.image.rotateWithOffset(t,y,0,m):t.clone(),p=x5(-y,l),f=o?this.getBoxForPalmLandmarks(d.palmLandmarks,p):d,g=_3(f,x,[this.inputSize,this.inputSize]),P=r.div(g,O.tf255);r.dispose(g),r.dispose(x);let[v,u]=this.handPoseModel.execute(P);An=T(),r.dispose(P);let b=(await v.data())[0];if(r.dispose(v),b>=n.hand.minConfidence/4){let j=r.reshape(u,[-1,3]),k=await j.array();r.dispose(u),r.dispose(j);let L=this.transformRawCoords(k,f,y,p),W=this.getBoxForHandLandmarks(L);this.storedBoxes[c]={...W,confidence:b};let X={landmarks:L,confidence:b,boxConfidence:d.confidence,fingerConfidence:b,box:{topLeft:W.startPoint,bottomRight:W.endPoint}};i.push(X)}else this.storedBoxes[c]=null;r.dispose(u)}else{let y=Z2(X2(d),rn),l={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:y.startPoint,bottomRight:y.endPoint},landmarks:[]};i.push(l)}}return this.storedBoxes=this.storedBoxes.filter(c=>c!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var an={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]},Ie,Ne,ln;async function f5(e,t){let n=await ln.estimateHands(e,t);if(!n)return[];let o=[];for(let s=0;s<n.length;s++){let A={};if(n[s].landmarks)for(let y of Object.keys(an))A[y]=an[y].map(l=>n[s].landmarks[l]);let a=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],c=[0,0,0,0];if(a&&a.length>0){for(let y of a)y[0]<i[0]&&(i[0]=y[0]),y[1]<i[1]&&(i[1]=y[1]),y[0]>i[2]&&(i[2]=y[0]),y[1]>i[3]&&(i[3]=y[1]);i[2]-=i[0],i[3]-=i[1],c=[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=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],c=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let d=G2(a);o.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:c,keypoints:a,annotations:A,landmarks:d})}return o}async function cn(e){var n,o;M.initial&&(Ie=null,Ne=null),!Ie||!Ne?[Ie,Ne]=await Promise.all([e.hand.enabled?N((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?N((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&h("cached model:",Ie.modelUrl),e.debug&&h("cached model:",Ne.modelUrl));let t=Ie?new q2(Ie):void 0;return t&&Ne&&(ln=new U2(t,Ne)),[Ie,Ne]}var r0=[null,null],Gs=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],he=[[0,0],[0,0]],Vs=["hand","fist","pinch","point","face","tip","pinchtip"],xn=4,yn=1.6,Zs=512,Xs=1.4,Y2=Number.MAX_SAFE_INTEGER,m5=0,te=[0,0],o0={boxes:[],hands:[]},fn={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 mn(e){var t;if(M.initial&&(r0[0]=null),r0[0])e.debug&&h("cached model:",r0[0].modelUrl);else{h2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),r0[0]=await N((t=e.hand.detector)==null?void 0:t.modelPath);let n=r0[0].executor?Object.values(r0[0].modelSignature.inputs):void 0;he[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[0]}async function pn(e){var t;if(M.initial&&(r0[1]=null),r0[1])e.debug&&h("cached model:",r0[1].modelUrl);else{r0[1]=await N((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=r0[1].executor?Object.values(r0[1].modelSignature.inputs):void 0;he[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[1]}async function qs(e,t){let n=[];if(!e||!r0[0])return n;let o={},s=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Zs),a=Math.round(A*s/8)*8;o.resize=r.image.resizeBilinear(e,[A,a]),o.cast=r.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await r0[0].executeAsync(o.cast,Gs),o.boxes=r.squeeze(o.rawBoxes,[0,2]),o.scores=r.squeeze(o.rawScores,[0]);let i=r.unstack(o.scores,1);r.dispose(i[xn]),i.splice(xn,1),o.filtered=r.stack(i,1),r.dispose(i),o.max=r.max(o.filtered,1),o.argmax=r.argMax(o.filtered,1);let c=0;o.nms=await r.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await o.nms.data(),y=await o.max.data(),l=await o.argmax.data();for(let m of Array.from(d)){let x=r.slice(o.boxes,m,1),p=await x.data();r.dispose(x);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],g=k2(f,Xs),P=[Math.trunc(f[0]*te[0]),Math.trunc(f[1]*te[1]),Math.trunc(f[2]*te[0]),Math.trunc(f[3]*te[1])],v=y[m],u=Vs[l[m]],b={id:c++,score:v,box:P,boxRaw:g,label:u};n.push(b)}return Object.keys(o).forEach(m=>r.dispose(o[m])),n.sort((m,x)=>x.score-m.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function p5(e,t,n){let o={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&&r0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=r.image.cropAndResize(e,[A],[0],[he[1][0],he[1][1]],"bilinear"),s.div=r.div(s.crop,O.tf255),[s.score,s.keypoints]=r0[1].execute(s.div,["Identity_1","Identity"]);let a=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(n.hand.minConfidence||0)){o.fingerScore=i,s.reshaped=r.reshape(s.keypoints,[-1,3]);let y=(await s.reshaped.array()).map(l=>[l[0]/he[1][1],l[1]/he[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);o.keypoints=y.map(l=>[te[0]*(l[0]+t.boxRaw[0]),te[1]*(l[1]+t.boxRaw[1]),l[2]||0]),o.landmarks=G2(o.keypoints);for(let l of Object.keys(fn))o.annotations[l]=fn[l].map(m=>o.landmarks&&o.keypoints[m]?o.keypoints[m]:null)}Object.keys(s).forEach(c=>r.dispose(s[c]))}return o}async function u5(e,t){var s,A;if(!((s=r0[0])!=null&&s.executor)||!((A=r0[1])!=null&&A.executor)||!r0[0].inputs[0].shape||!r0[1].inputs[0].shape)return[];te=[e.shape[2]||0,e.shape[1]||0],Y2++;let n=(t.hand.skipTime||0)>T()-m5,o=Y2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?o0.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>T()-m5,c=Y2<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(y=>p5(e,y,t))):t.skipAllowed&&i&&c&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(y=>p5(e,y,t))):(o0.boxes=await qs(e,t),m5=T(),o0.hands=await Promise.all(o0.boxes.map(y=>p5(e,y,t))),Y2=0);let d=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let y=0;y<o0.hands.length;y++){let l=y1(o0.hands[y].keypoints,te);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&o0.hands[y].fingerScore&&o0.hands[y].fingerScore>(t.hand.minConfidence||0)){let m=k2(l.box,yn),x=k2(l.boxRaw,yn);o0.boxes.push({...d[y],box:m,boxRaw:x})}}for(let y=0;y<o0.hands.length;y++){let l=_0(o0.hands[y].keypoints,te);o0.hands[y].box=l.box,o0.hands[y].boxRaw=l.boxRaw}a(o0.hands)})}var ne=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var s2={};re(s2,{connected:()=>J2,horizontal:()=>h5,kpt:()=>K2,relative:()=>g5,vertical:()=>b5});var K2=["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"]],b5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],g5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],J2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var S=ne(),T5=0;function hn(e,t){var a,i,c,d,y,l,m,x,p,f,g,P,v,u,b,j,k,L,W,X,D,V,J,U,q,f0;let n=T();if(!e)return ne();let o=Date.now()-e.timestamp,s=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(S.canvas=e.canvas),e.error&&(S.error=e.error),!S.body||e.body.length!==S.body.length)S.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let i0=e.body[R].box.map((C,F)=>((s-1)*S.body[R].box[F]+C)/s),b0=e.body[R].boxRaw.map((C,F)=>((s-1)*S.body[R].boxRaw[F]+C)/s),e0=e.body[R].keypoints.map((C,F)=>{var P0,t0,oe,Ke,Oe,F5,B5,H5,G5;return{score:C.score,part:C.part,position:[S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].position[0]||0)+(C.position[0]||0))/s:C.position[0],S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].position[1]||0)+(C.position[1]||0))/s:C.position[1],S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].position[2]||0)+(C.position[2]||0))/s:C.position[2]],positionRaw:[S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].positionRaw[0]||0)+(C.positionRaw[0]||0))/s:C.positionRaw[0],S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].positionRaw[1]||0)+(C.positionRaw[1]||0))/s:C.positionRaw[1],S.body[R].keypoints[F]?((s-1)*(S.body[R].keypoints[F].positionRaw[2]||0)+(C.positionRaw[2]||0))/s:C.positionRaw[2]],distance:[S.body[R].keypoints[F]?((s-1)*(((P0=S.body[R].keypoints[F].distance)==null?void 0:P0[0])||0)+(((t0=C.distance)==null?void 0:t0[0])||0))/s:(oe=C.distance)==null?void 0:oe[0],S.body[R].keypoints[F]?((s-1)*(((Ke=S.body[R].keypoints[F].distance)==null?void 0:Ke[1])||0)+(((Oe=C.distance)==null?void 0:Oe[1])||0))/s:(F5=C.distance)==null?void 0:F5[1],S.body[R].keypoints[F]?((s-1)*(((B5=S.body[R].keypoints[F].distance)==null?void 0:B5[2])||0)+(((H5=C.distance)==null?void 0:H5[2])||0))/s:(G5=C.distance)==null?void 0:G5[2]]}}),H={},z={connected:{}};(a=t.body.modelPath)!=null&&a.includes("efficientpose")?z=z2:(i=t.body.modelPath)!=null&&i.includes("blazepose")?z=M2:(c=t.body.modelPath)!=null&&c.includes("movenet")&&(z=s2);for(let[C,F]of Object.entries(z.connected)){let P0=[];for(let t0=0;t0<F.length-1;t0++){let oe=e0.find(Oe=>Oe.part===F[t0]),Ke=e0.find(Oe=>Oe.part===F[t0+1]);oe&&Ke&&P0.push([oe.position,Ke.position])}H[C]=P0}S.body[R]={...e.body[R],box:i0,boxRaw:b0,keypoints:e0,annotations:H}}if(!S.hand||e.hand.length!==S.hand.length)S.hand=JSON.parse(JSON.stringify(e.hand));else for(let R=0;R<e.hand.length;R++){let i0=e.hand[R].box.map((z,C)=>((s-1)*S.hand[R].box[C]+z)/s),b0=e.hand[R].boxRaw.map((z,C)=>((s-1)*S.hand[R].boxRaw[C]+z)/s);S.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(S.hand[R].keypoints=e.hand[R].keypoints);let e0=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((z,C)=>z.map((F,P0)=>((s-1)*(S.hand[R].keypoints[C][P0]||1)+(F||0))/s)):[],H={};if(Object.keys(S.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)S.hand[R].annotations=e.hand[R].annotations,H=S.hand[R].annotations;else if(e.hand[R].annotations)for(let z of Object.keys(e.hand[R].annotations))H[z]=(l=(y=(d=e.hand[R])==null?void 0:d.annotations)==null?void 0:y[z])!=null&&l[0]?e.hand[R].annotations[z].map((C,F)=>C.map((P0,t0)=>((s-1)*S.hand[R].annotations[z][F][t0]+P0)/s)):null;S.hand[R]={...e.hand[R],box:i0,boxRaw:b0,keypoints:e0,annotations:H}}if(!S.face||e.face.length!==S.face.length)S.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let i0=e.face[R].box.map((H,z)=>((s-1)*S.face[R].box[z]+H)/s),b0=e.face[R].boxRaw.map((H,z)=>((s-1)*S.face[R].boxRaw[z]+H)/s),e0=e.face[R].annotations;if(Object.keys(S.face[R].annotations).length!==Object.keys(e.face[R].annotations).length)S.face[R].annotations=e.face[R].annotations,e0=S.face[R].annotations;else if(e.face[R].annotations)for(let H of Object.keys(e.face[R].annotations))e0[H]=(p=(x=(m=e.face[R])==null?void 0:m.annotations)==null?void 0:x[H])!=null&&p[0]?e.face[R].annotations[H].map((z,C)=>z.map((F,P0)=>((s-1)*S.face[R].annotations[H][C][P0]+F)/s)):null;if(e.face[R].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(f=e.face[R].rotation)==null?void 0:f.matrix,H.angle={roll:((s-1)*(((P=(g=S.face[R].rotation)==null?void 0:g.angle)==null?void 0:P.roll)||0)+(((u=(v=e.face[R].rotation)==null?void 0:v.angle)==null?void 0:u.roll)||0))/s,yaw:((s-1)*(((j=(b=S.face[R].rotation)==null?void 0:b.angle)==null?void 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A=e[0][s],a=Math.round(100*A[51+4])/100;if(a>t.body.minConfidence){let i=[];for(let l=0;l<17;l++){let m=A[3*l+2];if(m>t.body.minConfidence){let x=[A[3*l+1],A[3*l+0]];i.push({part:K2[l],score:Math.round(100*m)/100,positionRaw:x,position:[Math.round((n.shape[2]||0)*x[0]),Math.round((n.shape[1]||0)*x[1])]})}}let c=_0(i.map(l=>l.position),[n.shape[2],n.shape[1]]),d={};for(let[l,m]of Object.entries(J2)){let x=[];for(let p=0;p<m.length-1;p++){let f=i.find(P=>P.part===m[p]),g=i.find(P=>P.part===m[p+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&x.push([f.position,g.position])}d[l]=x}let y={id:s,score:a,box:c.box,boxRaw:c.boxRaw,keypoints:[...i],annotations:d};P5(y),o.push(y)}}return o.sort((s,A)=>A.score-s.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function w5(e,t){var s;if(!(A0!=null&&A0.executor)||!((s=A0==null?void 0:A0.inputs)!=null&&s[0].shape))return[];t.skipAllowed||(Le.boxes.length=0),k5++;let n=(t.body.skipTime||0)>T()-Le.last,o=k5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?Le.bodies:new Promise(async A=>{let a={};k5=0,a.input=Mn(e,Q2),a.res=A0==null?void 0:A0.execute(a.input),Le.last=T();let i=await a.res.array();Le.bodies=a.res.shape[2]===17?Qs(i,t,e):_s(i,t,e);for(let c of Le.bodies)Pn(c,[e.shape[2]||1,e.shape[1]||1]),Rn(c.keypoints);Object.keys(a).forEach(c=>r.dispose(a[c])),A(Le.bodies)})}var H0,_2=[],En=0,E5=Number.MAX_SAFE_INTEGER,et=0,$2=2.5;async function zn(e){if(!H0||M.initial){H0=await N(e.object.modelPath);let t=H0!=null&&H0.executor?Object.values(H0.modelSignature.inputs):void 0;et=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&h("cached model:",H0.modelUrl);return H0}async function $s(e,t,n){var d,y;let o=0,s=[],A=et;for(let l of[1,2,4]){let m=l*13,x=r.squeeze(e.find(u=>u.shape[1]===m**2&&(u.shape[2]||0)===De.length)),p=await 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a2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],eA=a2.length,A2=a2.reduce((e,t,n)=>(e[t]=n,e),{}),tA=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],K7=tA.map(([e,t])=>[A2[e],A2[t]]),jn=[["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 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r&&r.getBackend()==="tensorflow"){let s=r.node.decodeJpeg(n),A=r.expandDims(s,0);e.tf.dispose(s),o=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function uA(e){let t;return typeof createImageBitmap=="function"?t=await fA(e):typeof Image!="undefined"||M.Canvas!==void 0?t=await mA(e):t=await pA(e),t}async function hA(e){var i,c,d,y;if(!r.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=r.getBackend(),n=r.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;r.env().set("ENGINE_COMPILE_ONLY",!0);let o=r.engine().state.numTensors,s=[];for(let[l,m]of Object.entries(e.models.models)){if(!m)continue;let x=m!=null&&m.modelSignature&&((c=(i=m==null?void 0:m.inputs)==null?void 0:i[0])!=null&&c.shape)?[...m.inputs[0].shape]:[1,64,64,3],u=m!=null&&m.modelSignature&&((y=(d=m==null?void 0:m.inputs)==null?void 0:d[0])!=null&&y.dtype)?m.inputs[0].dtype:"float32";for(let g=0;g<x.length;g++)x[g]===-1&&(x[g]=g===0?1:64);let f=r.zeros(x,u);try{let g=m.execute(f);s.push(l),Array.isArray(g)?g.forEach(P=>r.dispose(P)):r.dispose(g)}catch(g){e.config.debug&&h("compile fail model:",l)}r.dispose(f)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:s,kernels:A.length}),r.env().set("ENGINE_COMPILE_ONLY",!1);let a=r.engine().state.numTensors;a-o>0&&h("tensor leak:",a-o)}async function Un(e,t){await t2(e,!1);let n=T();return e.state="warmup",t&&(e.config=Q(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?ne():new Promise(async o=>{await e.models.load(),await hA(e);let s=await uA(e),A=T();e.config.debug&&h("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),o(s)})}var Ke,c2,d2,At,be,Yn=class{constructor(t){E(this,"version");E(this,"config");E(this,"result");E(this,"state");E(this,"process");E(this,"tf");E(this,"env",M);E(this,"draw",Tt);E(this,"match",P5);E(this,"models");E(this,"events");E(this,"faceTriangulation");E(this,"faceUVMap");E(this,"performance");q0(this,Ke,void 0);q0(this,c2,void 0);q0(this,d2,void 0);E(this,"analyze",(...t)=>{if(!k0(this,c2))return;let n=this.tf.engine().state.numTensors,o=k0(this,Ke);Q0(this,Ke,n);let s=n-o;s!==0&&h(...t,s)});q0(this,At,t=>{if(!k0(this,d2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof r.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});E(this,"webcam",new h2);E(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});q0(this,be,{});let n=(Qe.tfjs||r.version_core).replace(/-(.*)/,"");Ce.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ce.modelBasePath=M.browser?"../models/":"file://models/",this.version=yt,Object.defineProperty(this,"version",{value:yt}),this.config=JSON.parse(JSON.stringify(Ce)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Q(this.config,t)),o1(this.config),this.tf=r,this.state="idle",Q0(this,Ke,0),Q0(this,c2,!1),Q0(this,d2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new l2(this),gt(),this.result=ne(),this.process={tensor:null,canvas:null},this.faceTriangulation=K1,this.faceUVMap=J1,ot(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ce)),this.config.backend=t,dt(),M.initial=!0}validate(t){let n=it(Ce,t||this.config);return n.length===0&&(this.config=Q(this.config,t)),n}now(){return T()}image(t,n=!1){return m2(t,this.config,n)}async segmentation(t,n){var A,a,i;if(n&&(this.config=Q(this.config,n)),!this.config.segmentation.enabled)return null;let o=await m2(t,this.config);if(!o.tensor)return null;let s=null;return(A=this.config.segmentation.modelPath)!=null&&A.includes("rvm")&&(s=await Hn(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("meet")&&(s=await bn(o.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("selfie")&&(s=await Vn(o.tensor,this.config)),r.dispose(o.tensor),s}compare(t,n){return n1(this.config,t,n)}async init(){await t2(this,!0),await this.tf.ready(),dt()}async load(t){this.state="load";let n=T(),o=Object.values(this.models.models).filter(a=>a).length;t&&(this.config=Q(this.config,t)),this.env.initial&&(await t2(this,!1)||h("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(a=>a).length!==o&&(this.models.validate(),this.emit("load"));let A=Math.trunc(T()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return hn(t,this.config)}async warmup(t){let n=T(),o=await Un(this,t),s=T();return this.performance.warmup=Math.trunc(s-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),s={},A=0;for(let i of o.kernels){let c=Number(i.kernelTimeMs)||0;s[i.name]?s[i.name]+=c:s[i.name]=c,A+=c}let a=[];Object.entries(s).forEach(i=>a.push({kernel:i[0],time:i[1],perc:0}));for(let i of a)i.perc=Math.round(1e3*i.time/A)/1e3,i.time=Math.round(1e3*i.time)/1e3;return a.sort((i,c)=>c.time-i.time),a.length=20,a}async detect(t,n){return this.state="detect",new Promise(async o=>{var g,P,v,p,b,j,k,N,D,X,W,V,J,U,q,f0,R,i0,b0,e0,H;this.state="config";let s;this.config=Q(this.config,n),this.state="check";let A=k0(this,At).call(this,t);A&&(h(A,t),this.emit("error"),o(ne(A)));let a=T();await this.load(),s=T(),this.state="image";let i=await m2(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(T()-s):Math.trunc(T()-s),this.analyze("Get Image:"),!i.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o(ne("could not convert input to tensor"));return}this.emit("image"),s=T(),this.config.skipAllowed=await t1(this.config,i.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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(T()-s):Math.trunc(T()-s),this.analyze("Check Changed:");let c=[],d=[],y=[],l=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?c5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(s=T(),c=this.config.face.enabled?await c5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let m=this.config.body.maxDetected===-1?Q(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?d=this.config.body.enabled?O5(i.tensor,m):[]:(P=this.config.body.modelPath)!=null&&P.includes("blazepose")?d=this.config.body.enabled?kt(i.tensor,m):[]:(v=this.config.body.modelPath)!=null&&v.includes("efficientpose")?d=this.config.body.enabled?Nt(i.tensor,m):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(d=this.config.body.enabled?E5(i.tensor,m):[]),this.performance.body&&delete this.performance.body):(s=T(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?d=this.config.body.enabled?await O5(i.tensor,m):[]:(j=this.config.body.modelPath)!=null&&j.includes("blazepose")?d=this.config.body.enabled?await kt(i.tensor,m):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?d=this.config.body.enabled?await Nt(i.tensor,m):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(d=this.config.body.enabled?await E5(i.tensor,m):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?Q(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?((X=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&X.includes("handdetect")?y=this.config.hand.enabled?m5(i.tensor,x):[]:(V=(W=this.config.hand.detector)==null?void 0:W.modelPath)!=null&&V.includes("handtrack")&&(y=this.config.hand.enabled?h5(i.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(s=T(),(U=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&U.includes("handdetect")?y=this.config.hand.enabled?await m5(i.tensor,x):[]:(f0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&f0.includes("handtrack")&&(y=this.config.hand.enabled?await h5(i.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((R=this.config.object.modelPath)!=null&&R.includes("nanodet")?l=this.config.object.enabled?S5(i.tensor,this.config):[]:(i0=this.config.object.modelPath)!=null&&i0.includes("centernet")&&(l=this.config.object.enabled?zt(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(s=T(),(b0=this.config.object.modelPath)!=null&&b0.includes("nanodet")?l=this.config.object.enabled?await S5(i.tensor,this.config):[]:(e0=this.config.object.modelPath)!=null&&e0.includes("centernet")&&(l=this.config.object.enabled?await zt(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,d,y,l]=await Promise.all([c,d,y,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(s=T(),u=[...U3(c),...q3(d),...K3(y),...Y3(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(T()-a):Math.trunc(T()-a);let f=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:c,body:d,hand:y,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return qn(c,d,y,u,f)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(k0(this,be)[t.id]||(this.config.debug&&h("video start",t.id),k0(this,be)[t.id]=!0),!t.paused&&k0(this,be)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),k0(this,be)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&h("video stop",t.id),k0(this,be)[t.id]=!1)}};Ke=new WeakMap,c2=new WeakMap,d2=new WeakMap,At=new WeakMap,be=new WeakMap;export{u2 as Env,Yn as Human,Yn as default,Ce as defaults,Tt as draw,ne as empty,M as env,P5 as match,Xn as models};
+2Q==`;async function fA(e){let t=(s,A="application/octet-stream")=>fetch(`data:${A};base64,${s}`).then(a=>a.blob()),n,o;switch(e.config.warmup){case"face":n=await t(rt);break;case"body":case"full":n=await t(st);break;default:n=null}if(n){let s=await createImageBitmap(n);o=await e.detect(s,e.config),s.close()}return o}async function mA(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+rt;break;case"full":case"body":n="data:image/jpeg;base64,"+st;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(M.Image)o=new M.Image;else{t(void 0);return}o.onload=async()=>{let s=W0(o.naturalWidth,o.naturalHeight);if(!s)h("Warmup: Canvas not found"),t(void 0);else{let A=s.getContext("2d");A&&A.drawImage(o,0,0);let a=await e.image(s,!0),i=a.tensor?await e.detect(a.tensor,e.config):void 0;t(i)}},n?o.src=n:t(void 0)})}async function pA(e){let t=s=>Buffer.from(s,"base64"),n;e.config.warmup==="face"?n=t(rt):n=t(st);let o;if("node"in r&&r.getBackend()==="tensorflow"){let s=r.node.decodeJpeg(n),A=r.expandDims(s,0);e.tf.dispose(s),o=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function uA(e){let t;return typeof createImageBitmap=="function"?t=await fA(e):typeof Image!="undefined"||M.Canvas!==void 0?t=await mA(e):t=await pA(e),t}async function hA(e){var i,c,d,y;if(!r.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=r.getBackend(),n=r.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;r.env().set("ENGINE_COMPILE_ONLY",!0);let o=r.engine().state.numTensors,s=[];for(let[l,m]of Object.entries(e.models.models)){if(!m)continue;let x=m!=null&&m.modelSignature&&((c=(i=m==null?void 0:m.inputs)==null?void 0:i[0])!=null&&c.shape)?[...m.inputs[0].shape]:[1,64,64,3],p=m!=null&&m.modelSignature&&((y=(d=m==null?void 0:m.inputs)==null?void 0:d[0])!=null&&y.dtype)?m.inputs[0].dtype:"float32";for(let g=0;g<x.length;g++)x[g]===-1&&(x[g]=g===0?1:64);let f=r.zeros(x,p);try{let g=m.execute(f);s.push(l),Array.isArray(g)?g.forEach(P=>r.dispose(P)):r.dispose(g)}catch(g){e.config.debug&&h("compile fail model:",l)}r.dispose(f)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:s,kernels:A.length}),r.env().set("ENGINE_COMPILE_ONLY",!1);let a=r.engine().state.numTensors;a-o>0&&h("tensor leak:",a-o)}async function Un(e,t){await e2(e,!1);let n=T();return e.state="warmup",t&&(e.config=Q(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?ne():new Promise(async o=>{await e.models.load(),await hA(e);let s=await uA(e),A=T();e.config.debug&&h("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),o(s)})}var Ye,l2,c2,At,be,Yn=class{constructor(t){E(this,"version");E(this,"config");E(this,"result");E(this,"state");E(this,"process");E(this,"tf");E(this,"env",M);E(this,"draw",Tt);E(this,"match",M5);E(this,"models");E(this,"events");E(this,"faceTriangulation");E(this,"faceUVMap");E(this,"performance");q0(this,Ye,void 0);q0(this,l2,void 0);q0(this,c2,void 0);E(this,"analyze",(...t)=>{if(!k0(this,l2))return;let n=this.tf.engine().state.numTensors,o=k0(this,Ye);Q0(this,Ye,n);let s=n-o;s!==0&&h(...t,s)});q0(this,At,t=>{if(!k0(this,c2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof r.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});E(this,"webcam",new u2);E(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});q0(this,be,{});let n=(Je.tfjs||r.version_core).replace(/-(.*)/,"");Ce.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ce.modelBasePath=M.browser?"../models/":"file://models/",this.version=yt,Object.defineProperty(this,"version",{value:yt}),this.config=JSON.parse(JSON.stringify(Ce)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Q(this.config,t)),n1(this.config),this.tf=r,this.state="idle",Q0(this,Ye,0),Q0(this,l2,!1),Q0(this,c2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new i2(this),gt(),this.result=ne(),this.process={tensor:null,canvas:null},this.faceTriangulation=K1,this.faceUVMap=J1,ot(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ce)),this.config.backend=t,dt(),M.initial=!0}validate(t){let n=it(Ce,t||this.config);return n.length===0&&(this.config=Q(this.config,t)),n}now(){return T()}image(t,n=!1){return f2(t,this.config,n)}async segmentation(t,n){var A,a,i;if(n&&(this.config=Q(this.config,n)),!this.config.segmentation.enabled)return null;let o=await f2(t,this.config);if(!o.tensor)return null;let s=null;return(A=this.config.segmentation.modelPath)!=null&&A.includes("rvm")&&(s=await Hn(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("meet")&&(s=await bn(o.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("selfie")&&(s=await Vn(o.tensor,this.config)),r.dispose(o.tensor),s}compare(t,n){return t1(this.config,t,n)}async init(){await e2(this,!0),await this.tf.ready(),dt()}async load(t){this.state="load";let n=T(),o=Object.values(this.models.models).filter(a=>a).length;t&&(this.config=Q(this.config,t)),this.env.initial&&(await e2(this,!1)||h("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(a=>a).length!==o&&(this.models.validate(),this.emit("load"));let A=Math.trunc(T()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return hn(t,this.config)}async warmup(t){let n=T(),o=await Un(this,t),s=T();return this.performance.warmup=Math.trunc(s-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),s={},A=0;for(let i of o.kernels){let c=Number(i.kernelTimeMs)||0;s[i.name]?s[i.name]+=c:s[i.name]=c,A+=c}let a=[];Object.entries(s).forEach(i=>a.push({kernel:i[0],time:i[1],perc:0}));for(let i of a)i.perc=Math.round(1e3*i.time/A)/1e3,i.time=Math.round(1e3*i.time)/1e3;return a.sort((i,c)=>c.time-i.time),a.length=20,a}async detect(t,n){return this.state="detect",new Promise(async o=>{var g,P,v,u,b,j,k,L,W,X,D,V,J,U,q,f0,R,i0,b0,e0,H;this.state="config";let s;this.config=Q(this.config,n),this.state="check";let A=k0(this,At).call(this,t);A&&(h(A,t),this.emit("error"),o(ne(A)));let a=T();await this.load(),s=T(),this.state="image";let i=await f2(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(T()-s):Math.trunc(T()-s),this.analyze("Get Image:"),!i.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o(ne("could not convert input to tensor"));return}this.emit("image"),s=T(),this.config.skipAllowed=await e1(this.config,i.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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(T()-s):Math.trunc(T()-s),this.analyze("Check Changed:");let c=[],d=[],y=[],l=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?l5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(s=T(),c=this.config.face.enabled?await l5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let m=this.config.body.maxDetected===-1?Q(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?d=this.config.body.enabled?L5(i.tensor,m):[]:(P=this.config.body.modelPath)!=null&&P.includes("blazepose")?d=this.config.body.enabled?kt(i.tensor,m):[]:(v=this.config.body.modelPath)!=null&&v.includes("efficientpose")?d=this.config.body.enabled?Nt(i.tensor,m):[]:(u=this.config.body.modelPath)!=null&&u.includes("movenet")&&(d=this.config.body.enabled?w5(i.tensor,m):[]),this.performance.body&&delete this.performance.body):(s=T(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?d=this.config.body.enabled?await L5(i.tensor,m):[]:(j=this.config.body.modelPath)!=null&&j.includes("blazepose")?d=this.config.body.enabled?await kt(i.tensor,m):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?d=this.config.body.enabled?await Nt(i.tensor,m):[]:(L=this.config.body.modelPath)!=null&&L.includes("movenet")&&(d=this.config.body.enabled?await w5(i.tensor,m):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?Q(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?((X=(W=this.config.hand.detector)==null?void 0:W.modelPath)!=null&&X.includes("handdetect")?y=this.config.hand.enabled?f5(i.tensor,x):[]:(V=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&V.includes("handtrack")&&(y=this.config.hand.enabled?u5(i.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(s=T(),(U=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&U.includes("handdetect")?y=this.config.hand.enabled?await f5(i.tensor,x):[]:(f0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&f0.includes("handtrack")&&(y=this.config.hand.enabled?await u5(i.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((R=this.config.object.modelPath)!=null&&R.includes("nanodet")?l=this.config.object.enabled?z5(i.tensor,this.config):[]:(i0=this.config.object.modelPath)!=null&&i0.includes("centernet")&&(l=this.config.object.enabled?zt(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(s=T(),(b0=this.config.object.modelPath)!=null&&b0.includes("nanodet")?l=this.config.object.enabled?await z5(i.tensor,this.config):[]:(e0=this.config.object.modelPath)!=null&&e0.includes("centernet")&&(l=this.config.object.enabled?await zt(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,d,y,l]=await Promise.all([c,d,y,l])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(s=T(),p=[...U3(c),...q3(d),...K3(y),...Y3(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(T()-a):Math.trunc(T()-a);let f=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:c,body:d,hand:y,gesture:p,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return qn(c,d,y,p,f)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(k0(this,be)[t.id]||(this.config.debug&&h("video start",t.id),k0(this,be)[t.id]=!0),!t.paused&&k0(this,be)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),k0(this,be)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&h("video stop",t.id),k0(this,be)[t.id]=!1)}};Ye=new WeakMap,l2=new WeakMap,c2=new WeakMap,At=new WeakMap,be=new WeakMap;export{p2 as Env,Yn as Human,Yn as default,Ce as defaults,Tt as draw,ne as empty,M as env,M5 as match,Xn as models};
diff --git a/dist/human.esm.js b/dist/human.esm.js
index b6b739c8..c712b7d1 100644
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
@@ -38308,10 +38308,10 @@ var getRawBox = (box, input) => box ? [
   (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),
   (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0)
 ] : [0, 0, 0, 0];
-var scaleBoxCoordinates = (box, factor) => {
+var scaleBoxCoordinates = (box, factor, anchor) => {
   const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
   const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
-  const landmarks = box.landmarks;
+  const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);
   return { startPoint, endPoint, landmarks, confidence: box.confidence };
 };
 var cutAndResize = (box, image, cropSize) => {
@@ -38558,7 +38558,9 @@ async function getBoxes(inputImage, config3) {
         landmarks: await b.landmarks.array(),
         confidence
       };
-      const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]);
+      b.anchor = Ke(anchors, [nms[i], 0], [1, 2]);
+      const anchor = await b.anchor.data();
+      const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4], anchor);
       const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
       const squaredBox = squarifyBox(enlargedBox);
       if (squaredBox.size[0] > (((_e = config3.face.detector) == null ? void 0 : _e["minSize"]) || 0) && squaredBox.size[1] > (((_f2 = config3.face.detector) == null ? void 0 : _f2["minSize"]) || 0))
@@ -38787,10 +38789,7 @@ async function predict4(input, config3) {
       face4.boxRaw = getRawBox(box, input);
       face4.score = face4.boxScore;
       face4.size = box.size;
-      face4.mesh = box.landmarks.map((pt) => [
-        (box.startPoint[0] + box.endPoint[0]) / 2 + pt[0] * input.shape[2] / size(),
-        (box.startPoint[1] + box.endPoint[1]) / 2 + pt[1] * input.shape[1] / size()
-      ]);
+      face4.mesh = box.landmarks;
       face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
       for (const key of Object.keys(blazeFaceLandmarks))
         face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
@@ -38809,14 +38808,11 @@ async function predict4(input, config3) {
       face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
       if (face4.faceScore < (((_g2 = config3.face.detector) == null ? void 0 : _g2.minConfidence) || 1)) {
         box.confidence = face4.faceScore;
-        if (config3.face.mesh.keepInvalid) {
+        if (config3.face.mesh["keepInvalid"]) {
           face4.box = clampBox(box, input);
           face4.boxRaw = getRawBox(box, input);
           face4.score = face4.boxScore;
-          face4.mesh = box.landmarks.map((pt) => [
-            (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
-            (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
-          ]);
+          face4.mesh = box.landmarks;
           face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]);
           for (const key of Object.keys(blazeFaceLandmarks)) {
             face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map
index f6934049..e1e54d10 100644
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["tfjs.esm.js", "../src/util/util.ts", "../src/config.ts", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/util/webcam.ts", "../models/models.json", "../src/tfjs/load.ts", "../package.json", "../src/tfjs/humangl.ts", "../src/tfjs/constants.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/face/facemeshcoords.ts", "../src/face/constants.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/draw/labels.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/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/face/iris.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/gear/emotion.ts", "../src/face/faceres.ts", "../src/face/mask.ts", "../src/face/antispoof.ts", "../src/face/liveness.ts", "../src/gear/gear.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/mobilefacenet.ts", "../src/face/insightface.ts", "../src/face/angles.ts", "../src/face/anthropometry.ts", "../src/face/face.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/gesture/gesture.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/result.ts", "../src/body/movenetcoords.ts", "../src/util/interpolate.ts", "../src/segmentation/meet.ts", "../src/face/match.ts", "../src/models.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/rvm.ts", "../src/segmentation/selfie.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
-  "sourcesContent": ["/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nvar PV=Object.create;var yb=Object.defineProperty;var MV=Object.getOwnPropertyDescriptor;var LV=Object.getOwnPropertyNames;var BV=Object.getPrototypeOf,VV=Object.prototype.hasOwnProperty;var Kt=(r,e)=>()=>(e||r((e={exports:{}}).exports,e),e.exports),Ge=(r,e)=>{for(var t in e)yb(r,t,{get:e[t],enumerable:!0})},zV=(r,e,t,o)=>{if(e&&typeof e==\"object\"||typeof e==\"function\")for(let n of LV(e))!VV.call(r,n)&&n!==t&&yb(r,n,{get:()=>e[n],enumerable:!(o=MV(e,n))||o.enumerable});return r};var ap=(r,e,t)=>(t=r!=null?PV(BV(r)):{},zV(e||!r||!r.__esModule?yb(t,\"default\",{value:r,enumerable:!0}):t,r));var i0=Kt((Sse,a0)=>{a0.exports=It;var yo=null;try{yo=new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0,97,115,109,1,0,0,0,1,13,2,96,0,1,127,96,4,127,127,127,127,1,127,3,7,6,0,1,1,1,1,1,6,6,1,127,1,65,0,11,7,50,6,3,109,117,108,0,1,5,100,105,118,95,115,0,2,5,100,105,118,95,117,0,3,5,114,101,109,95,115,0,4,5,114,101,109,95,117,0,5,8,103,101,116,95,104,105,103,104,0,0,10,191,1,6,4,0,35,0,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,126,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,127,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,128,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,129,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,130,34,4,66,32,135,167,36,0,32,4,167,11])),{}).exports}catch(r){}function It(r,e,t){this.low=r|0,this.high=e|0,this.unsigned=!!t}It.prototype.__isLong__;Object.defineProperty(It.prototype,\"__isLong__\",{value:!0});function Mr(r){return(r&&r.__isLong__)===!0}It.isLong=Mr;var QI={},ZI={};function eu(r,e){var t,o,n;return e?(r>>>=0,(n=0<=r&&r<256)&&(o=ZI[r],o)?o:(t=vt(r,(r|0)<0?-1:0,!0),n&&(ZI[r]=t),t)):(r|=0,(n=-128<=r&&r<128)&&(o=QI[r],o)?o:(t=vt(r,r<0?-1:0,!1),n&&(QI[r]=t),t))}It.fromInt=eu;function bo(r,e){if(isNaN(r))return e?Ji:Co;if(e){if(r<0)return Ji;if(r>=r0)return s0}else{if(r<=-e0)return Pr;if(r+1>=e0)return n0}return r<0?bo(-r,e).neg():vt(r%Ep|0,r/Ep|0,e)}It.fromNumber=bo;function vt(r,e,t){return new It(r,e,t)}It.fromBits=vt;var Dm=Math.pow;function Db(r,e,t){if(r.length===0)throw Error(\"empty string\");if(r===\"NaN\"||r===\"Infinity\"||r===\"+Infinity\"||r===\"-Infinity\")return Co;if(typeof e==\"number\"?(t=e,e=!1):e=!!e,t=t||10,t<2||36<t)throw RangeError(\"radix\");var o;if((o=r.indexOf(\"-\"))>0)throw Error(\"interior hyphen\");if(o===0)return Db(r.substring(1),e,t).neg();for(var n=bo(Dm(t,8)),s=Co,a=0;a<r.length;a+=8){var i=Math.min(8,r.length-a),p=parseInt(r.substring(a,a+i),t);if(i<8){var u=bo(Dm(t,i));s=s.mul(u).add(bo(p))}else s=s.mul(n),s=s.add(bo(p))}return s.unsigned=e,s}It.fromString=Db;function rs(r,e){return typeof r==\"number\"?bo(r,e):typeof r==\"string\"?Db(r,e):vt(r.low,r.high,typeof e==\"boolean\"?e:r.unsigned)}It.fromValue=rs;var JI=1<<16,cz=1<<24,Ep=JI*JI,r0=Ep*Ep,e0=r0/2,t0=eu(cz),Co=eu(0);It.ZERO=Co;var Ji=eu(0,!0);It.UZERO=Ji;var $p=eu(1);It.ONE=$p;var o0=eu(1,!0);It.UONE=o0;var Rb=eu(-1);It.NEG_ONE=Rb;var n0=vt(-1,2147483647,!1);It.MAX_VALUE=n0;var s0=vt(-1,-1,!0);It.MAX_UNSIGNED_VALUE=s0;var Pr=vt(0,-2147483648,!1);It.MIN_VALUE=Pr;var de=It.prototype;de.toInt=function(){return this.unsigned?this.low>>>0:this.low};de.toNumber=function(){return this.unsigned?(this.high>>>0)*Ep+(this.low>>>0):this.high*Ep+(this.low>>>0)};de.toString=function(e){if(e=e||10,e<2||36<e)throw 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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 o=e[t],{success:n,asyncInit:s}=this.initializeBackend(o);if(s||n)return{name:o,asyncInit:s}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let o=this.state.tensorInfo.get(t),n=o.backend,s=this.readSync(t),a=n.refCount(t);n.disposeData(t,!0),o.backend=e,e.move(t,s,o.shape,o.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let o=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\");o=e}let n;return this.scopedRun(()=>this.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return vi.nextTensorId++}nextVariableId(){return vi.nextVariableId++}clone(e){let t=T.runKernel(mo,{x:e}),o={x:e},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(co,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(Kc(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Gb(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Gb(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Kc(f,this.backendName);$(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let w=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,w);o=this.saveTensorsForBackwardMode(k)}return w}}else{let{forwardFunc:f}=e,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Gb(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,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Eb(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?($(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=e;o===\"string\"&&Oo(e[0])&&(s=e.map(p=>Ii(p)));let a=n.write(s,t,o),i=new it(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=vb(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||\"float32\";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new it(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new Na(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*up(e.dtype)),this.state.numBytes+=o,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:o})),e instanceof Na||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 o=e.size*up(e.dtype);this.state.numBytes-=o}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,o=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-o;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,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},p=Eb(e);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=lp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,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=Qc(e),o=new Set(t.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let a=this.state.activeScope.track[s];!a.kept&&!o.has(a.id)&&a.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(s=>{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if($(t.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));$(s instanceof it,()=>\"The result y returned by f() must be a tensor.\");let a=l0(this.state.activeTape,t,s);if(!n&&a.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[s.id]=o==null?Nz(s.shape):o,m0(i,a,u=>this.tidy(u),Tz);let p=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(e){return $(gs(e),()=>\"The f passed in customGrad(f) must be a function.\"),(...t)=>{$(t.every(i=>i instanceof it),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),$(o.value instanceof it,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),$(gs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];$(c.length===t.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),$(c.every(m=>m instanceof it),()=>\"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 l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,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=ou(),o=await this.backend.time(e);return o.wallMs=ou()-t,o}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 Mm;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}};vi.nextTensorId=0;vi.nextVariableId=0;function Nz(r){let e=Wc(We(r),\"float32\");return T.makeTensor(e,r,\"float32\")}function Hb(){let r=Tb();if(r._tfengine==null){let e=new Uc(r);r._tfengine=new vi(e)}return jI(r._tfengine.ENV),g0(()=>r._tfengine),r._tfengine}var T=Hb();function Tz(r,e){let t={a:r,b:e};return T.runKernel(eo,t)}var ki={};Ge(ki,{isBrowser:()=>qb,isMobile:()=>Ez,mockIsMobile:()=>$z});function _z(){return typeof navigator!=\"undefined\"&&navigator!=null}var Kb;function $z(r){Kb=r}function Ez(r){if(Kb!==void 0)return Kb;if(r||_z()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let e=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!e){let t=r;return 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a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),$(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),$(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),$(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&$(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&$(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),_i(a,i,p,c,u,s)}var uv=N({batchNorm3d_:AW});function RW(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),$(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),$(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),$(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&$(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&$(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),_i(a,i,p,c,u,s)}var pv=N({batchNorm4d_:RW});function DW(r,e,t){let o=v(r,\"x\",\"bincount\"),n=v(e,\"weights\",\"bincount\");$(o.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),$(t>=0,()=>`size must be non-negative, but got ${t}.`),$(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, 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s={x:v(r,\"x\",\"cumsum\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(jo,s,a)}var kv=N({cumsum_:ZW});function JW(r,e,t,o=!1){let n=v(r,\"x\",\"denseBincount\"),s=v(e,\"weights\",\"denseBincount\");$(n.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),$(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),$(t>=0,()=>`size must be non-negative, but got ${t}.`),$(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return T.runKernel(ni,a,i)}var Nv=N({denseBincount_:JW});function eU(r,e,t=\"NHWC\"){let o=v(r,\"x\",\"depthToSpace\",\"float32\"),n=t===\"NHWC\"?o.shape[1]:o.shape[2],s=t===\"NHWC\"?o.shape[2]:o.shape[3],a=t===\"NHWC\"?o.shape[3]:o.shape[1];$(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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V1(r,e=!1){return T.tidy(()=>{$(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=ed(t),s=Lr(r),a=Oi([[1]],[1,1]),i=Lr(a),p=t>=o?o:t;for(let u=0;u<p;++u){let c=s,l=i,m=n;[i,s,n]=T.tidy(()=>{let d=Ke(s,[u,u],[t-u,1]),f=iu(d),h=Ke(s,[u,u],[1,1]),g=ss(uu(h,0),Oi([[-1]]),Oi([[1]])),x=ke(h,ne(g,f)),b=He(d,x);b.shape[0]===1?i=Lr(a):i=xt([a,Ke(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=Cr(He(Xe(g,x),f)),w=Ke(s,[u,0],[t-u,o]),k=ne(C,i),_=Gp(i);if(u===0)s=ke(w,Xe(k,Xe(_,w)));else{let R=ke(w,Xe(k,Xe(_,w)));s=xt([Ke(s,[0,0],[u,o]),R],0)}let E=Gp(k),A=Ke(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=ke(A,Xe(Xe(A,i),E));else{let R=ke(A,Xe(Xe(A,i),E));n=xt([Ke(n,[0,0],[t,u]),R],1)}return[i,s,n]}),Ot([c,l,m])}return!e&&t>o&&(n=Ke(n,[0,0],[t,o]),s=Ke(s,[0,0],[o,o])),[n,s]})}var z1=N({qr_:DH});var $t;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})($t||($t={}));function FH(r,e,t=$t.SUM_BY_NONZERO_WEIGHTS){let o=v(r,\"losses\",\"computeWeightedLoss\"),n=null;e!=null&&(n=v(e,\"weights\",\"computeWeightedLoss\"));let s=n==null?o:ne(o,n);if(t===$t.NONE)return s;if(t===$t.SUM)return et(s);if(t===$t.MEAN){if(n==null)return cu(s);{let a=o.size/n.size,i=He(et(s),et(n));return a>1?He(i,Ce(a)):i}}if(t===$t.SUM_BY_NONZERO_WEIGHTS){if(n==null)return He(et(s),Ce(o.size));{let a=ne(n,Hs(o.shape)),i=je(et(hd(a,Ce(0))),\"float32\");return He(et(s),i)}}throw Error(`Unknown reduction: ${t}`)}var ar=N({computeWeightedLoss_:FH});function OH(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"absoluteDifference\"),s=v(e,\"predictions\",\"absoluteDifference\"),a=null;t!=null&&(a=v(t,\"weights\",\"absoluteDifference\")),gt(n.shape,s.shape,\"Error in absoluteDifference: \");let i=Yt(ke(n,s));return ar(i,a,o)}var W1=N({absoluteDifference_:OH});function PH(r,e,t,o,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"cosineDistance\"),a=v(e,\"predictions\",\"cosineDistance\"),i=null;o!=null&&(i=v(o,\"weights\",\"cosineDistance\")),gt(s.shape,a.shape,\"Error in cosineDistance: \");let p=Ce(1),u=ke(p,et(ne(s,a),t,!0));return ar(u,i,n)}var U1=N({cosineDistance_:PH});function MH(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"hingeLoss\"),s=v(e,\"predictions\",\"hingeLoss\"),a=null;t!=null&&(a=v(t,\"weights\",\"hingeLoss\")),gt(n.shape,s.shape,\"Error in hingeLoss: \");let i=Ce(1);n=ke(ne(Ce(2),n),i);let p=Fi(ke(i,ne(n,s)));return ar(p,a,o)}var G1=N({hingeLoss_:MH});function LH(r,e,t,o=1,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"huberLoss\"),a=v(e,\"predictions\",\"huberLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"huberLoss\")),gt(s.shape,a.shape,\"Error in huberLoss: \");let p=Ce(o),u=Yt(ke(a,s)),c=fd(u,p),l=ke(u,c),m=be(ne(Ce(.5),Qt(c)),ne(p,l));return ar(m,i,n)}var H1=N({huberLoss_:LH});function BH(r,e,t,o=1e-7,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"logLoss\"),a=v(e,\"predictions\",\"logLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"logLoss\")),gt(s.shape,a.shape,\"Error in logLoss: \");let p=Ce(1),u=Ce(o),c=Cr(ne(s,Da(be(a,u)))),l=ne(ke(p,s),Da(be(ke(p,a),u))),m=ke(c,l);return ar(m,i,n)}var K1=N({logLoss_:BH});function VH(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"meanSquaredError\"),s=v(e,\"predictions\",\"meanSquaredError\"),a=null;t!=null&&(a=v(t,\"weights\",\"meanSquaredError\")),gt(n.shape,s.shape,\"Error in meanSquaredError: \");let i=_d(n,s);return ar(i,a,o)}var q1=N({meanSquaredError_:VH});function zH(r,e){let t=v(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),o=v(e,\"logits\",\"sigmoidCrossEntropyWithLogits\");gt(t.shape,o.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let n=Fi(o),s=ne(o,t),a=sd(So(Cr(Yt(o))));return be(ke(n,s),a)}function WH(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"multiClassLabels\",\"sigmoidCrossEntropy\"),a=v(e,\"logits\",\"sigmoidCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"sigmoidCrossEntropy\")),gt(s.shape,a.shape,\"Error in sigmoidCrossEntropy: \"),o>0){let u=Ce(o),c=Ce(1),l=Ce(.5);s=be(ne(s,ke(c,u)),ne(l,u))}let p=zH(s,a);return ar(p,i,n)}var j1=N({sigmoidCrossEntropy_:WH});function UH(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return br((n,s,a)=>{let p=ud(s,[t],!0),u=ke(je(s,\"float32\"),p);a([n,u]);let c=Cr(ne(u,n));return{value:et(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=Ea(d.shape,[t]);return[ne(W(d,x),ke(je(h,\"float32\"),So(g))),ne(W(d,x),ke(So(g),je(h,\"float32\")))]}}})(r,e)}function GH(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"onehotLabels\",\"softmaxCrossEntropy\"),a=v(e,\"logits\",\"softmaxCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"softmaxCrossEntropy\")),gt(s.shape,a.shape,\"Error in softmaxCrossEntropy: \"),o>0){let u=Ce(o),c=Ce(1),l=Ce(s.shape[1]);s=be(ne(s,ke(c,u)),He(u,l))}let p=UH(s,a);return ar(p,i,n)}var X1=N({softmaxCrossEntropy_:GH});function HH(r,e,t,o){let n=v(r,\"indices\",\"sparseFillEmptyRows\",\"int32\"),s=v(e,\"values\",\"sparseFillEmptyRows\"),a=v(t,\"denseShape\",\"sparseFillEmptyRows\",\"int32\"),i=v(o,\"defaultValue\",\"sparseFillEmptyRows\",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape\n        ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(hi,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var Y1=N({sparseFillEmptyRows_:HH});function KH(r,e,t){let o=v(r,\"inputIndices\",\"sparseReshape\",\"int32\"),n=v(e,\"inputShape\",\"sparseReshape\",\"int32\"),s=v(t,\"newShape\",\"sparseReshape\",\"int32\");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape\n        ${o.shape}`);if(n.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(va,a);return{outputIndices:i[0],outputShape:i[1]}}var Q1=N({sparseReshape_:KH});function qH(r,e,t){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(e,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n          ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n          ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(gi,a)}var Z1=N({sparseSegmentMean_:qH});function jH(r,e,t){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(e,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n         ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n         ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(xi,a)}var J1=N({sparseSegmentSum_:jH});function XH(r,e,t,o,n,s,a,i){let p=v(r,\"data\",\"stringNGrams\",\"string\");if(p.dtype!==\"string\")throw new Error(\"Data must be of datatype string\");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,\"dataSplits\",\"stringNGrams\");if(u.dtype!==\"int32\")throw new Error(\"Data splits must be of datatype int32\");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(Ds,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var eN=N({stringNGrams_:XH});function YH(r,e,t=!0){let o=v(r,\"input\",\"stringSplit\",\"string\"),n=v(e,\"delimiter\",\"stringSplit\",\"string\");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(Ci,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var tN=N({stringSplit_:YH});function QH(r,e){let t=v(r,\"input\",\"stringToHashBucketFast\",\"string\"),o={numBuckets:e};if(e<=0)throw new Error(\"Number of buckets must be at least 1\");let n={input:t};return T.runKernel(Si,n,o)}var rN=N({stringToHashBucketFast_:QH});var ZH={fft:zp,ifft:du,rfft:Wp,irfft:Td},JH={hammingWindow:C1,hannWindow:Fd,frame:Od,stft:S1},eK={flipLeftRight:I1,grayscaleToRGB:v1,resizeNearestNeighbor:O1,resizeBilinear:F1,rotateWithOffset:k1,cropAndResize:w1,nonMaxSuppression:N1,nonMaxSuppressionAsync:$1,nonMaxSuppressionWithScore:E1,nonMaxSuppressionWithScoreAsync:A1,nonMaxSuppressionPadded:R1,nonMaxSuppressionPaddedAsync:D1,threshold:P1,transform:M1},tK={bandPart:L1,gramSchmidt:B1,qr:z1},rK={absoluteDifference:W1,computeWeightedLoss:ar,cosineDistance:U1,hingeLoss:G1,huberLoss:H1,logLoss:K1,meanSquaredError:q1,sigmoidCrossEntropy:j1,softmaxCrossEntropy:X1},oK={sparseFillEmptyRows:Y1,sparseReshape:Q1,sparseSegmentMean:Z1,sparseSegmentSum:J1},nK={stringNGrams:eN,stringSplit:tN,stringToHashBucketFast:rN};var oN={};Ge(oN,{Serializable:()=>il,SerializationMap:()=>qs,registerClass:()=>IC});var il=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},qs=class{constructor(){this.classNameMap={}}static getMap(){return qs.instance==null&&(qs.instance=new qs),qs.instance}static register(e){qs.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function IC(r){$(r.className!=null,()=>\"Class being registered does not have the static className property defined.\"),$(typeof r.className==\"string\",()=>\"className is required to be a string, but got type \"+typeof r.className),$(r.className.length>0,()=>\"Class being registered has an empty-string as its className, which is disallowed.\"),qs.register(r)}var wr=class extends il{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ot(s),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 pC(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:Ce(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var yu=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}static get className(){return\"Adadelta\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=be(ne(p,this.rho),ne(Qt(i),1-this.rho)),l=ne(He($r(be(u,this.epsilon)),$r(be(p,this.epsilon))),i),m=be(ne(u,this.rho),ne(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=be(ne(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var bu=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}static get className(){return\"Adagrad\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Us(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=be(i,Qt(a));i.assign(p);let u=be(ne(He(a,$r(be(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var Cu=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=Ce(t).variable(),this.accBeta2=Ce(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}static get className(){return\"Adam\"}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=ke(1,this.accBeta1),n=ke(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=be(ne(c,this.beta1),ne(u,1-this.beta1)),d=be(ne(l,this.beta2),ne(Qt(u),1-this.beta2)),f=He(m,o),h=He(d,n);c.assign(m),l.assign(d);let g=be(ne(He(f,be($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ne(this.accBeta1,this.beta1)),this.accBeta2.assign(ne(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(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),Ee(()=>{this.accBeta1.assign(Aa(this.beta1,this.iterations_+1)),this.accBeta2.assign(Aa(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}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)}};var Su=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}static get className(){return\"Adamax\"}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=ke(1,this.accBeta1),n=He(-this.learningRate,be(ne(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=be(ne(c,this.beta1),ne(u,1-this.beta1)),d=ne(l,this.beta2),f=Yt(u),h=dd(d,f);c.assign(m),l.assign(h);let g=be(ne(He(n,o),He(m,be(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(ne(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(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)}};var Pa=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}static get className(){return\"SGD\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=be(ne(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(Ce(-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 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TypeError(`Node type ${r.op} is not implemented`)}};function Vr(r,e,t=\"\"){if(!(typeof r==\"number\"||typeof e==\"number\")){y.assert(r.length===e.length,()=>t+` Shapes ${r} and ${e} must match`);for(let o=0;o<r.length;o++){let n=r[o],s=e[o];y.assert(n<0||s<0||n===s,()=>t+` Shapes ${r} and ${e} must match`)}}}function PN(r){return!(typeof r==\"number\"||r.some(e=>e<0))}function Hp(r,e,t){let o=of(r,t),n=!PN(o);if(n&&e.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);if(n&&e.forEach(s=>{o=of(s.shape,o)}),!PN(o))throw new Error(`Non-fully-defined elementShape: ${o}`);return o}function of(r,e){if(typeof r==\"number\")return e;if(typeof e==\"number\")return r;if(r.length!==e.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`);let t=[];for(let o=0;o<r.length;++o){let n=r[o],s=e[o];if(n>=0&&s>=0&&n!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`);t[o]=n>=0?n:s}return t}var 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xt(n,0)})}};function MN(r,e,t){let o=r.dtype;if(r.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`);if(r.dtype!==t)throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t}`);let n=r.shape.slice(1);Vr(n,e,\"TensorList shape mismatch: \");let s=so(r);return new Ba(s,e,o)}function LN(r,e,t,o){return new Ba([],r,e,o)}function BN(r,e,t,o){if(e.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);let n=Math.max(...e);if(o!=null&&o!==-1&&n>=o)throw new Error(`Max index must be < array size (${n}  vs. ${o})`);let s=new Ba([],t,r.dtype,o),a=so(r,0);return e.forEach((i,p)=>{s.setItem(i,a[p])}),s}function VN(r,e,t){let o=0,n=e.map(c=>(o+=c,o));if(o!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${o}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=of(s,t),i=o===0?0:r.size/o,p=Ee(()=>{let c=[];r=W(r,[1,o,i]);for(let l=0;l<e.length;++l){let d=[0,l===0?0:n[l-1],0],f=[1,e[l],i];c[l]=W(Ke(r,d,f),a)}return r.dispose(),c}),u=new Ba([],t,r.dtype,e.length);for(let c=0;c<p.length;c++)u.setItem(c,p[c]);return u}var zN=async(r,e,t)=>{switch(r.op){case\"If\":case\"StatelessIf\":{let o=I(\"thenBranch\",r,e,t),n=I(\"elseBranch\",r,e,t),s=I(\"cond\",r,e,t),a=I(\"args\",r,e,t);return(await s.data())[0]?t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case\"While\":case\"StatelessWhile\":{let o=I(\"body\",r,e,t),n=I(\"cond\",r,e,t),s=I(\"args\",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),p=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;p[0];){let c=u;u=await 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o=I(\"size\",r,e,t),n=I(\"dtype\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"dynamicSize\",r,e,t),i=I(\"clearAfterRead\",r,e,t),p=I(\"identicalElementShapes\",r,e,t),u=I(\"name\",r,e,t),c=new nf(u,n,o,s,p,a,i);return t.addTensorArray(c),[c.idTensor,Ce(1)]}case\"TensorArrayWriteV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case\"TensorArrayReadV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"index\",r,e,t);return[t.getTensorArray(o.id).read(n)]}case\"TensorArrayGatherV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"dtype\",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case\"TensorArrayScatterV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case\"TensorArrayConcatV3\":{let 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o=I(\"indices\",r,e,t),n=I(\"tensor\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"numElements\",r,e,t),i=BN(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let o=I(\"elementShape\",r,e,t),n=I(\"elementDType\",r,e,t),s;r.op===\"TensorListReserve\"?s=\"numElements\":s=\"maxNumElements\";let a=I(s,r,e,t),i=r.op===\"TensorListReserve\"?-1:a,p=LN(o,n,a,i);return t.addTensorList(p),[p.idTensor]}case\"TensorListGather\":{let o=I(\"tensorListId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case\"TensorListStack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=MN(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=VN(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[Ce(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function WN(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=ll(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var UN=(r,e,t,o=Ye)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=ll(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=WN(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=WN(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=ll(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=ll(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GN=(r,e,t,o=Ye)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function rS(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var HN=async(r,e,t,o,n=Ye)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=rS(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=rS(r,e,t),c=I(\"padToMaxOutputSize\",r,e,t),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=rS(r,e,t);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case\"Where\":{let s=n.cast(I(\"condition\",r,e,t),\"bool\"),a=[await n.whereAsync(s)];return s.dispose(),a}case\"ListDiff\":return n.setdiff1dAsync(I(\"x\",r,e,t),I(\"y\",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KN=(r,e,t,o=Ye)=>{switch(r.op){case\"LowerBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.lowerBound(n,s)]}case\"TopKV2\":{let n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qN=(r,e,t,o=Ye)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Gt(r.name,e,t)||n];case\"Placeholder\":return[Gt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[is(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>is(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[is(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,u));return[a];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sf=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ce(0),this.tensorMap=new Map,_r(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 Ce(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=so(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i<s;i++){let p=o[i],u=n[i];_r(u),this.tensorMap.set(p,u)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return Ee(()=>{let n=[];for(let s=0;s<o.length;s++){let a=o[s],i=this.findWithDefault(a,t);n.push(i)}return Sr(n)})}findWithDefault(e,t){let o=this.tensorMap.get(e);return o!=null?o: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}`)}};var jN=async(r,e,t,o)=>{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XN=(r,e,t,o=Ye)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YN=(r,e,t,o=Ye)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QN=(r,e,t,o=Ye)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZN=(r,e,t,o=Ye)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];case\"SparseToDense\":return[o.sparseToDense(I(\"sparseIndices\",r,e,t),I(\"outputShape\",r,e,t),I(\"sparseValues\",r,e,t),I(\"defaultValue\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JN=(r,e,t,o=Ye)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eT=(r,e,t,o=Ye)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tT=(r,e,t,o=Ye)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i<n.length;i++)n[i]&&s.push(i);let a=I(\"x\",r,e,t);return[o.reverse(a,s)]}case\"ReverseV2\":{let n=I(\"axis\",r,e,t),s=I(\"x\",r,e,t);return[o.reverse(s,n)]}case\"Slice\":{let n=I(\"begin\",r,e,t),s=I(\"size\",r,e,t);return[o.slice(I(\"x\",r,e,t),n,s)]}case\"StridedSlice\":{let n=I(\"begin\",r,e,t),s=I(\"end\",r,e,t),a=I(\"strides\",r,e,t),i=I(\"beginMask\",r,e,t),p=I(\"endMask\",r,e,t),u=I(\"ellipsisMask\",r,e,t),c=I(\"newAxisMask\",r,e,t),l=I(\"shrinkAxisMask\",r,e,t),m=I(\"x\",r,e,t);return[o.stridedSlice(m,n,s,a,i,p,u,c,l)]}case\"Pack\":return Ee(()=>{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rT=(r,e,t,o=Ye)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oT=(r,e,t,o=Ye)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nT=(r,e,t,o=Ye)=>{switch(r.op){case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sT=(r,e,t,o=Ye)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function oS(r,e,t,o,n=Ee){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>FN(a,i,p));case\"basic_math\":return n(()=>ON(a,i,p));case\"control\":return zN(a,i,p);case\"convolution\":return n(()=>UN(a,i,p));case\"creation\":return n(()=>GN(a,i,p));case\"dynamic\":return HN(a,i,p);case\"evaluation\":return n(()=>KN(a,i,p));case\"image\":return n(()=>XN(a,i,p));case\"graph\":return n(()=>qN(a,i,p));case\"logical\":return n(()=>YN(a,i,p));case\"matrices\":return n(()=>QN(a,i,p));case\"normalization\":return n(()=>ZN(a,i,p));case\"ragged\":return n(()=>JN(a,i,p));case\"reduction\":return n(()=>eT(a,i,p));case\"slice_join\":return n(()=>tT(a,i,p));case\"sparse\":return n(()=>rT(a,i,p));case\"spectral\":return n(()=>oT(a,i,p));case\"string\":return n(()=>nT(a,i,p));case\"transformation\":return n(()=>sT(a,i,p));case\"hash_table\":return jN(a,i,p,o);case\"custom\":let u=Gd(a.op);if(u&&u.customExecutor)return u.customExecutor(new rf(a,i,p));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()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var dl=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,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 o=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(o))}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 nS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=Object.keys(r).map(m=>Ir(m)[0]),c=[];o!=null&&(c=o.map(m=>Ir(m.name)[0]));let l=[...e];for(;l.length>0;){let m=l.pop();if((sS(m)||T6(m)||_6(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function aT(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Ir(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let p=new Set,u=[];for(;s.length>0;){let c=s.pop();p.add(c.name),e[c.name]||u.push(c),c.children.forEach(l=>{!p.has(l.name)&&o.has(l.name)&&l.inputs.every(m=>p.has(m.name))&&s.push(l)})}return u}var v6=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],k6=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],N6=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function sS(r){return v6.indexOf(r.op)>=0}function T6(r){return k6.indexOf(r.op)>=0}function _6(r){return N6.indexOf(r.op)>=0}var ku=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new ku(e.functions[o],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(o=>e[o].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 o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+\"--\"+n.join(this.SEPERATOR)}compile(e,t){let o=nS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),p=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`)}return aT(this.graph,this.weightMap,o)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return _r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(l=>this.graph.nodes[Ir(l)[0]]),s=t.map(l=>Ir(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),p=this.compiledMap.get(i);p==null&&(p=this.compile(e,a),this.compiledMap.set(i,p));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){this.keepIntermediateTensors=!1,console.warn(l.message)}let u={},c={};return Ee(()=>{let l=new dl(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(h=>{let[g,x]=Ir(h),b=[];b[x]=e[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let d=this.getFrozenTensorIds(m),f={};for(let h=0;h<p.length;h++){let g=p[h];if(!m[g.name]){let x=oS(g,m,l,this._resourceManager);if(y.isPromise(x))throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);m[g.name]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[g.name]=this.cloneTensorList(x)),this.checkTensorForDisposal(g.name,g,m,l,d,s,f)}}return this.parent==null&&l.dispose(d),t.map(h=>Gt(h,m,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(o[e].forEach(p=>{p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length)}),t.inputs.forEach(p=>{if(p.category!==\"control\"){let u=EN(p.name,o,n);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new dl(this.weightMap,n,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Gt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(C=>this.graph.nodes[Ir(C)[0]]),i=o.map(C=>Ir(C)[0]),p=i.map(C=>this.graph.nodes[C]);p.length===0&&(p=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:m}=nS(e,p,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(C=>({node:C,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(C=>{let[w,k]=Ir(C),_=[];_[k]=e[C],f[w]=_});let h={},g=this.getFrozenTensorIds(f),x={};for(;d.length>0;){let C=this.processStack(a,d,t,f,x,g,i,h,u);await Promise.all(C)}l==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 b=p.filter(C=>!sS(C)&&!Gt(C.name,f,t)).map(C=>C.name);if(b.length>0){let C=\"\";throw l!=null&&(C=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`)}return f}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=as(l.node.name,o)),n[l.node.name]==null){let d=oS(l.node,n,o,this._resourceManager);m||([m]=as(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=as(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Ir(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Ir(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Ir(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var af=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]}};var $6=\"?tfjs-format=file\",E6=\"model.json\",fl=class{constructor(e,t={},o=La){this.modelUrl=e,this.loadOptions=t,this.version=\"n/a\",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new af}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ku(ml.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=ml.Instance.transformGraph(e.modelInitializer);this.initializer=new ku(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof it?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof it)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count 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t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n<o.length;n++){let s=o[n],a=t[s];this.resourceIdToCapturedInput[a.resourceId]=e[n]}}}execute(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=this.executor.execute(e,t);return o.length>1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return 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Zp={};Ge(Zp,{addImpl:()=>uS,bincountImpl:()=>jp,bincountReduceImpl:()=>uf,castImpl:()=>iS,ceilImpl:()=>pS,concatImpl:()=>Nu,equalImpl:()=>cS,expImpl:()=>mS,expm1Impl:()=>fS,floorImpl:()=>hS,gatherNdImpl:()=>pf,gatherV2Impl:()=>cf,greaterEqualImpl:()=>xS,greaterImpl:()=>gS,lessEqualImpl:()=>bS,lessImpl:()=>yS,linSpaceImpl:()=>lf,logImpl:()=>CS,maxImpl:()=>mf,maximumImpl:()=>SS,minimumImpl:()=>wS,multiplyImpl:()=>hl,negImpl:()=>IS,notEqualImpl:()=>vS,prodImpl:()=>kS,raggedGatherImpl:()=>df,raggedRangeImpl:()=>ff,raggedTensorToTensorImpl:()=>hf,rangeImpl:()=>_u,rsqrtImpl:()=>NS,scatterImpl:()=>Va,sigmoidImpl:()=>OT,simpleAbsImpl:()=>aS,sliceImpl:()=>$u,sparseFillEmptyRowsImpl:()=>gf,sparseReshapeImpl:()=>xf,sparseSegmentReductionImpl:()=>Qp,sqrtImpl:()=>LT,squaredDifferenceImpl:()=>_S,stridedSliceImpl:()=>yf,stringNGramsImpl:()=>Eu,stringSplitImpl:()=>Au,stringToHashBucketFastImpl:()=>Ru,subImpl:()=>ES,tileImpl:()=>bf,topKImpl:()=>Cf,transposeImpl:()=>Xp,uniqueImpl:()=>Sf});function 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No=S.RowPartitionType,Yp=class{constructor(e,t,o,n,s,a,i,p,u,c){this.shape=e,this.shapeShape=t,this.values=o,this.valuesShape=n,this.valuesDType=s,this.defaultValue=a,this.defaultValueShape=i,this.rowPartitionValues=p,this.rowPartitionValuesShapes=u,this.rowPartitionTypes=S.getRowPartitionTypesHelper(c),this.raggedRank=S.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===No.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===No.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){let t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case No.VALUE_ROWIDS:return Yp.getMaxWidthValueRowID(t);case No.ROW_SPLITS:return Yp.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${No[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){let 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firstDimension.\"),s}calculateOutputIndexRowSplit(e,t,o,n){let s=e.length,a=[];for(let i=0;i<s-1;++i){let p=e[i+1]-e[i],u=Math.min(n,p),c=t[i];c===-1&&(u=0);for(let l=0;l<u;++l)a.push(c),c+=o;for(let l=0;l<p-u;++l)a.push(-1)}if(s>0&&a.length!==e[s-1])throw new Error(\"Invalid row split size.\");return a}calculateOutputIndexValueRowID(e,t,o,n){let s=e.length,a=[];if(s===0)return[];let i=0,p=e[0];if(p>=t.length)throw new Error(`Got currentValueRowId=${p}, which is not less than ${t.length}`);let u=t[p];a.push(u);for(let c=1;c<s;++c){let l=e[c];if(l===p)u>=0&&(++i,i<n?u+=o:u=-1);else{if(i=0,p=l,l>=t.length)throw new Error(`Got nextValueRowId=${l} which is not less than ${t.length}`);u=t[l]}a.push(u)}if(a.length!==e.length)throw new Error(\"Invalid row ids.\");return a}calculateOutputIndex(e,t,o,n){let s=this.getRowPartitionTensor(e),a=this.getRowPartitionTypeByDimension(e);switch(a){case No.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,t,o,n);case No.ROW_SPLITS:if(s.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(s,t,o,n);default:throw new Error(`Unsupported partition type: ${No[a]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error(\"No row_partition_types given.\");let t=this.rowPartitionTypes[0];switch(t){case No.FIRST_DIM_SIZE:return e[0];case No.VALUE_ROWIDS:throw new Error(\"Cannot handle VALUE_ROWIDS in first dimension.\");case No.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${No[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error(\"Invalid first partition input. Tensor requires at least one element.\");let t=this.getFirstDimensionSize(),o=this.calculateOutputSize(t),n=new Array(this.raggedRank+1);n[n.length-1]=1;for(let p=n.length-2;p>=0;--p)n[p]=n[p+1]*o[p+1];let s=DT(o,!1),a=y.getArrayFromDType(this.valuesDType,y.sizeFromShape(s));if(n[0]*o[0]>0){let p=this.calculateFirstParentOutputIndex(t,n[0],o[0]);for(let u=1;u<=this.raggedRank;++u)p=this.calculateOutputIndex(u-1,p,n[u],o[u]);this.setOutput(this.raggedRank,p,a,s)}return[s,a]}setOutput(e,t,o,n){if(o.length===0)return;let s=this.values,a=o,i=n.slice();i=i.slice(e+1);let p=y.sizeFromShape(i),u=t.length,c=this.defaultValue;if(c.length!==p&&c.length!==1){let f=this.defaultValueShape;Ee(()=>{let h=W(c,f);c=$i(h,i).dataSync()})}let l=0,m=0,d=0;for(let f=0;f<=u;++f){let h=f<u?t[f]:-1;if(h===d){++d;continue}if(m<d){let g=s.subarray(l*p),x=a.subarray(m*p),b=(d-m)*p;RT(x,g,b)}if(f>=u){let g=o.length;h=Math.floor(g/p)}if(h>d)if(this.defaultValue.length===1)a.subarray(d*p,h*p).fill(this.defaultValue[0]),d=h;else for(;h>d;){let g=a.slice(d*p);RT(g,c,p),++d}h<0?(l=f+1,m=d):(l=f,m=d,d=m+1)}}};function RT(r,e,t){for(let o=0;o<t;o++)r[o]=e[o]}function DT(r,e){let t=[];for(let o of r){if(o<0){if(!e)throw new Error(`Dimension ${o} must be >= 0`);if(o<-1)throw new Error(`Dimension ${o} must be >= -1`);o=-1}t.push(o)}return t}function hf(r,e,t,o,n,s,a,i,p,u){return new Yp(r,e,t,o,n,s,a,i,p,u).compute()}function _u(r,e,t,o){let n=r===e,s=r<e&&t<0,a=e<r&&t>1;if(n||s||a)return y.makeZerosTypedArray(0,o);let i=Math.abs(Math.ceil((e-r)/t)),p=y.makeZerosTypedArray(i,o);e<r&&t===1&&(t=-1),p[0]=r;for(let u=1;u<p.length;u++)p[u]=p[u-1]+t;return p}var NS=vr(r=>1/Math.sqrt(r)),nj=ko(zn,NS),FT={kernelName:zn,backendName:\"cpu\",kernelFunc:nj};function Va(r,e,t,o,n,s,a,i,p,u){let c=[o/n,n],l=r.values,m=e.values;if(o===0)return le(t,e.dtype);let d=le(c,e.dtype);typeof p==\"string\"||typeof p==\"number\"?d.values.fill(p):typeof p==\"boolean\"&&d.values.fill(+p);for(let f=0;f<s;f++){let h=[],g=0;for(let x=0;x<a;x++){let b=l[f*a+x];h.push(b),g+=b*i[x]}if(g<0||g>=o/n)throw new Error(`Invalid indices: ${h} does not index into ${t}`);for(let x=0;x<n;x++)u?d.values[g*n+x]+=m[f*n+x]:d.values[g*n+x]=e.rank===0?m[0]:m[f*n+x]}return d}var OT=vr(r=>1/(1+Math.exp(-r))),TS=we(Gn,r=>1/(1+Math.exp(-r))),PT={kernelName:Gn,backendName:\"cpu\",kernelFunc:TS};function $u(r,e,t,o,n){let s=ut.isSliceContinous(o,e,t),a=y.sizeFromShape(t),i=y.computeStrides(o);if(s){let l=ut.computeFlatOffset(e,i);return n===\"string\"?r.slice(l,l+a):r.subarray(l,l+a)}let p=n===\"string\"?S.fromUint8ToStringArray(r):r,u=le(o,n,p),c=le(t,n);for(let l=0;l<c.size;++l){let m=c.indexToLoc(l),d=m.map((f,h)=>f+e[h]);c.set(u.get(...d),...m)}return n===\"string\"?S.fromStringArrayToUint8(c.values):c.values}function To(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o;j(n,\"slice\");let[i,p]=ut.parseSliceParams(n,s,a);ut.assertParamsValid(n,i,p);let u=t.data.get(n.dataId).values,c=$u(u,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,c)}var MT={kernelName:Es,backendName:\"cpu\",kernelFunc:To};function gf(r,e,t,o,n,s,a){let i=e[0],p=s[0],u=new Array(p),c=new Array(i),l=e[1];if(p===0){if(i!==0)throw new Error(S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));let g=y.getArrayFromDType(t,0),x=y.getArrayFromDType(n,0);return[g,[0,l],x,u,c]}let m=!0,d=0,f=new Array(p).fill(0);for(let g=0;g<i;++g){let x=r[g*l];if(x<0)throw new Error(S.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,x));if(x>=p)throw new Error(S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,x,p));++f[x],m=m&&x>=d,d=x}let h=!0;for(let g=0;g<p;++g){let x=f[g]===0;u[g]=x,h=h&&!x,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(h&&m){let g=r,x=o;for(let b=0;b<i;++b)c[b]=b;return[g,[i,l],x,u,c]}else{let 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m=o.length,d=[];if(m>0){d[m-1]=1;for(let g=m-2;g>=0;--g)d[g]=d[g+1]*o[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*p[g+1]}let h=y.getArrayFromDType(t,a*i);for(let g=0;g<a;++g){let x=0;for(let b=0;b<m;++b)x+=r[g*m+b]*d[b];for(let b=0;b<i;++b)h[g*i+b]=Math.trunc(x/f[b]),x%=f[b]}return[h,[a,i],p]}function Qp(r,e,t,o,n,s=!1,a=0){let i=o.length,p=[e[0],r.length/e[0]],u=p[1],l=i>0?n[i-1]+1:0;if(l<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=e.slice();m[0]=l;let d=m.reduce((C,w)=>C*w,1),f=y.getArrayFromDType(t,d);if(i===0)return l>0&&f.fill(a),[f,m];if(l<=0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let h=0,g=1,x=0,b=n[h];for(;;){let C=0;if(g<i){if(C=n[g],b===C){++g;continue}if(b>=C)throw new Error(S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(b<0||b>=l)throw new Error(S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b,l));b>x&&f.fill(a,x*u,b*u);for(let 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R2={kernelName:jo,backendName:\"cpu\",kernelFunc:Gj};function Hj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.data.get(n.dataId).values,u=t.data.get(s.dataId).values,c=jp(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=uf(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var D2={kernelName:ni,backendName:\"cpu\",kernelFunc:Hj};function Kj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(a===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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P2={kernelName:yp,backendName:\"cpu\",kernelFunc:qj};function jj(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o;j([n,s],\"depthwiseConv2DNativeBackpropInput\");let l=y.computeStrides(n.shape),m=y.computeStrides(s.shape),d=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),f=new st(d.inShape,\"float32\"),h=f.values,[g,x,b]=f.strides,C=t.data.get(n.dataId).values,[w,k,_]=l,E=t.data.get(s.dataId).values,[A,R,D]=m,{batchSize:P,filterHeight:M,filterWidth:L,inChannels:V,inHeight:z,inWidth:U,outChannels:K,outHeight:H,outWidth:q,strideHeight:Z,strideWidth:ee}=d,oe=M-1-d.padInfo.top,J=L-1-d.padInfo.left,te=K/V;for(let ie=0;ie<P;++ie)for(let ce=0;ce<V;++ce)for(let ye=0;ye<z;++ye){let Ne=ye-oe,Ie=Math.max(0,Math.ceil(Ne/Z)),Ae=Math.min(H,(M+Ne)/Z);for(let Re=0;Re<U;++Re){let ot=Re-J,pt=Math.max(0,Math.ceil(ot/ee)),ze=Math.min(q,(L+ot)/ee),ct=0;for(let at=Ie;at<Ae;++at){let ft=at*Z-Ne;for(let ht=pt;ht<ze;++ht){let 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Se=O();Se.registerFlag(\"HAS_WEBGL\",()=>Se.getNumber(\"WEBGL_VERSION\")>0);Se.registerFlag(\"WEBGL_VERSION\",()=>Df(2)?2:Df(1)?1:0);Se.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);Se.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>Se.get(\"WEBGL_VERSION\")===2);Se.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);Se.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);Se.registerFlag(\"WEBGL_PACK\",()=>Se.getBool(\"HAS_WEBGL\"));Se.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_CLIP\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_REDUCE\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_CONV_IM2COL\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>aw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>iw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let r=Se.getNumber(\"WEBGL_VERSION\");return r===0?0:uw(r)});Se.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>Se.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!ki.isMobile());Se.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>pw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>Se.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:Se.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));Se.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>cw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>lw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>Se.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);Se.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Se.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>ki.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Se.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);Se.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);Se.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);Se.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);Se.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);Se.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>Se.getBool(\"IS_TEST\"));Se.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);Se.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);Se.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);Se.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function wt(){let r,e,t,o,n,s,a,i,p,u;return O().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",e=\"in\",t=\"out\",o=\"in\",n=\"texture\",s=\"outputColor\",a=\"out vec4 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${t} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${a}; ${i};`}).join(\"\")}function s8(r,e){let t=r.length,o=r.map(s=>`${e}[${s}]`),n=new Array(t-1);n[t-2]=o[t-1];for(let s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}function A$(r,e,t=\"index\"){let o=r.map((s,a)=>a),n=s8(o,e);return n.map((s,a)=>{let i=`int ${r[a]} = ${t} / ${n[a]}`,p=a===n.length-1?`int ${r[a+1]} = ${t} - ${r[a]} * ${n[a]}`:`index -= ${r[a]} * ${n[a]}`;return`${i}; ${p};`}).join(\"\")}function sc(r){let e=y.computeStrides(r).map(t=>t.toString());return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;\n  }\n`}function ac(){return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`}var Ff=`\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;var{getBroadcastDims:R$}=S;function D$(r,e,t){let o=[];if(r.forEach(d=>{let f=y.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${f>1?`[${f}]`:\"\"};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`)),t.enableShapeUniforms){let{uniformShape:h}=Of(t.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(h.length){case 1:o.push(`uniform int ${d.name}Shape;`);break;case 2:o.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:o.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:o.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}o.push(`uniform ivec2 ${d.name}TexShape;`)}}),t.enableShapeUniforms){switch(e.logicalShape.length){case 1:o.push(\"uniform int outShape;\");break;case 2:o.push(\"uniform ivec2 outShape;\"),o.push(\"uniform int outShapeStrides;\");break;case 3:o.push(\"uniform ivec3 outShape;\"),o.push(\"uniform ivec2 outShapeStrides;\");break;case 4:o.push(\"uniform ivec4 outShape;\"),o.push(\"uniform ivec3 outShapeStrides;\");break;default:break}o.push(\"uniform ivec2 outTexShape;\")}t.customUniforms&&t.customUniforms.forEach(d=>{o.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:\"\"};`)});let n=o.join(`\n`),s=r.map(d=>a8(d,e,t.packedInputs,t.enableShapeUniforms)).join(`\n`),a=e.texShape,i=wt(),p=p8(i),u,c,l=m8(i);return e.isPacked?(u=i8(e.logicalShape,a,t.enableShapeUniforms),c=l8(i)):(u=u8(e.logicalShape,a,t.enableShapeUniforms),c=c8(i)),t.packedInputs&&(l+=g8),[l,p,c,n,u,s,t.userCode].join(`\n`)}function uc(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return _8(r,e);case 1:return E8(r,e);case 2:return R8(r,e);case 3:return F8(r,e);case 4:return P8(r,e);case 5:return M8(r);case 6:return L8(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function F$(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return T8(r);case 1:return $8(r,e);case 2:return A8(r,e);case 3:return D8(r,e);default:return O8(r,e)}}function a8(r,e,t=!1,o){let n=\"\";t?n+=F$(r,o):n+=uc(r,o);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?n+=B8(r,e):n+=V8(r,e)),n}function i8(r,e,t){switch(r.length){case 0:return O$();case 1:return x8(r,e,t);case 2:return k8(r,e,t);case 3:return b8(r,e,t);default:return S8(r,e,t)}}function u8(r,e,t){switch(r.length){case 0:return O$();case 1:return y8(r,e,t);case 2:return N8(r,e,t);case 3:return C8(r,e,t);case 4:return w8(r,e,t);case 5:return I8(r,e);case 6:return v8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function p8(r){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${r.texture2D}(textureSampler, uv).r;\n    }\n  `}function c8(r){return`\n    void setOutput(float val) {\n      ${r.output} = vec4(val, 0, 0, 0);\n    }\n  `}function l8(r){return`\n    void setOutput(vec4 val) {\n      ${r.output} = val;\n    }\n  `}function m8(r){return`${r.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${r.varyingFs} vec2 resultUV;\n    ${r.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    ${r.defineSpecialNaN}\n    ${r.defineSpecialInf}\n    ${r.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    ${d8}\n    ${f8}\n    ${h8}\n  `}var d8=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,f8=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,h8=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,g8=`\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;function O$(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function x8(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?`\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 * ${o[1]}.0);\n      }\n    `:o[1]===1?t?`\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 * ${o[0]}.0);\n      }\n    `:t?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${o[0]}, ${o[1]}));\n      return 2 * (resTexRC.x * ${o[1]} + resTexRC.y);\n    }\n  `}function y8(r,e,t){return e[0]===1?t?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${e[1]}.0);\n      }\n    `:e[1]===1?t?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${e[0]}.0);\n      }\n    `:t?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${e[0]}, ${e[1]}));\n      return resTexRC.x * ${e[1]} + resTexRC.y;\n    }\n  `}function b8(r,e,t){if(t)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[2]/2),s=n*Math.ceil(r[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${o[0]}, ${o[1]}));\n      int index = resTexRC.x * ${o[1]} + resTexRC.y;\n\n      int b = index / ${s};\n      index -= b * ${s};\n\n      int r = 2 * (index / ${n});\n      int c = imod(index, ${n}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function C8(r,e,t){if(t)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    ${Pu([\"r\",\"c\",\"d\"],r)}\n    return ivec3(r, c, d);\n  }\n`;let o=ps([\"r\",\"c\",\"d\"],r);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${e[0]}, ${e[1]}));\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n      ${o}\n      return ivec3(r, c, d);\n    }\n  `}function S8(r,e,t){if(t)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[r.length-1]/2),s=n*Math.ceil(r[r.length-2]/2),a=s,i=\"\",p=\"b, r, c\";for(let u=2;u<r.length-1;u++)a*=r[r.length-u-1],i=`\n      int b${u} = index / ${a};\n      index -= b${u} * ${a};\n    `+i,p=`b${u}, `+p;return`\n    ivec${r.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${o[0]}, ${o[1]}));\n      int index = resTexRC.x * ${o[1]} + resTexRC.y;\n\n      ${i}\n\n      int b = index / ${s};\n      index -= b * ${s};\n\n      int r = 2 * (index / ${n});\n      int c = imod(index, ${n}) * 2;\n\n      return ivec${r.length}(${p});\n    }\n  `}function w8(r,e,t){if(t)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      ${Pu([\"r\",\"c\",\"d\",\"d2\"],r)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let o=ps([\"r\",\"c\",\"d\",\"d2\"],r);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${e[0]}, ${e[1]}));\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n      ${o}\n      return ivec4(r, c, d, d2);\n    }\n  `}function I8(r,e){let t=ps([\"r\",\"c\",\"d\",\"d2\",\"d3\"],r);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},\n                             ${e[1]}));\n\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n\n      ${t}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function v8(r,e){let t=ps([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],r);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${e[0]}, ${e[1]}));\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n\n      ${t}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function k8(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return t?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${o[0]}, ${o[1]}));\n      }\n    `;let n=Math.ceil(r[1]/2);return t?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${o[0]}, ${o[1]}));\n\n      int index = resTexRC.x * ${o[1]} + resTexRC.y;\n      int r = 2 * (index / ${n});\n      int c = imod(index, ${n}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function N8(r,e,t){return y.arraysEqual(r,e)?t?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));\n      }\n    `:r[1]===1?t?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${e[0]}, ${e[1]}));\n        int index = resTexRC.x * ${e[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:r[0]===1?t?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${e[0]}, ${e[1]}));\n        int index = resTexRC.x * ${e[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:t?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${e[0]}, ${e[1]}));\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n      int r = index / ${r[1]};\n      int c = index - r * ${r[1]};\n      return ivec2(r, c);\n    }\n  `}function Mu(r){return`offset${r}`}function T8(r){let e=r.name,t=\"get\"+e.charAt(0).toUpperCase()+e.slice(1),o=wt();return`\n    vec4 ${t}() {\n      return ${o.texture2D}(${e}, halfCR);\n    }\n  `}function _8(r,e){let t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`float ${o}() {return ${t};}`;let[n,s]=r.shapeInfo.texShape;if(n===1&&s===1)return`\n      float ${o}() {\n        return sampleTexture(${t}, halfCR);\n      }\n    `;let a=Mu(t);if(e)return`\n    float ${o}() {\n      vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], ${a});\n      return sampleTexture(${t}, uv);\n    }\n  `;let[i,p]=r.shapeInfo.texShape;return`\n    float ${o}() {\n      vec2 uv = uvFromFlat(${i}, ${p}, ${a});\n      return sampleTexture(${t}, uv);\n    }\n  `}function $8(r,e){let t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=wt();if(e)return`\n    vec4 ${o}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${s.texture2D}(${t}, uv);\n    }\n  `;let a=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];return`\n    vec4 ${o}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${a[0]}, ${a[1]}, index);\n      return ${s.texture2D}(${t}, uv);\n    }\n  `}function E8(r,e){let t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`\n      float ${o}(int index) {\n        ${pc(r)}\n      }\n    `;let n=r.shapeInfo.texShape,s=n[0],a=n[1];if(a===1&&s===1)return`\n      float ${o}(int index) {\n        return sampleTexture(${t}, halfCR);\n      }\n    `;let i=Mu(t);return a===1?e?`\n      float ${o}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t}TexShape[0]));\n        return sampleTexture(${t}, uv);\n      }\n    `:`\n      float ${o}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `:s===1?e?`\n      float ${o}(int index) {\n        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t}TexShape[1]), 0.5);\n        return sampleTexture(${t}, uv);\n      }\n    `:`\n      float ${o}(int index) {\n        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${a}.0, 0.5);\n        return sampleTexture(${t}, uv);\n      }\n    `:e?`\n    float ${o}(int index) {\n      vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], index + ${i});\n      return sampleTexture(${t}, uv);\n    }\n  `:`\n    float ${o}(int index) {\n      vec2 uv = uvFromFlat(${s}, ${a}, index + ${i});\n      return sampleTexture(${t}, uv);\n    }\n  `}function A8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],p=wt();if(s!=null&&y.arraysEqual(t,s))return e?`\n      vec4 ${n}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);\n\n        return ${p.texture2D}(${o}, uv);\n      }\n    `:`\n      vec4 ${n}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${a}.0);\n\n        return ${p.texture2D}(${o}, uv);\n      }\n    `;if(e)return`\n    vec4 ${n}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${o}TexShape[0]) / 2.0), ceil(float(${o}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${o}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${p.texture2D}(${o}, uv);\n    }\n  `;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(t[1]/2);return`\n    vec4 ${n}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);\n      return ${p.texture2D}(${o}, uv);\n    }\n  `}function R8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(t,s)){if(e)return`\n      float ${n}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `;let m=s[0],d=s[1];return`\n    float ${n}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${m}.0);\n      return sampleTexture(${o}, uv);\n    }\n  `}let{newShape:a,keptDims:i}=y.squeezeShape(t),p=a;if(p.length<t.length){let m=cc(r,p),d=[\"row\",\"col\"];return`\n      ${uc(m,e)}\n      float ${n}(int row, int col) {\n        return ${n}(${lc(d,i)});\n      }\n    `}if(r.shapeInfo.isUniform)return`\n      float ${n}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));\n        ${pc(r)}\n      }\n    `;let u=s[0],c=s[1],l=Mu(o);return c===1?e?`\n      float ${n}(int row, int col) {\n        float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${o}TexShape[0]));\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n    float ${n}(int row, int col) {\n      float index = dot(vec3(row, col, ${l}), vec3(${t[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);\n      return sampleTexture(${o}, uv);\n    }\n  `:u===1?e?`\n      float ${n}(int row, int col) {\n        float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${o}TexShape[1]), 0.5);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n    float ${n}(int row, int col) {\n      float index = dot(vec3(row, col, ${l}), vec3(${t[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${o}, uv);\n    }\n  `:e?`\n      float ${n}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${o}Shape[1] + col + ${l};\n        vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n  float ${n}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${t[1]} + col + ${l};\n    vec2 uv = uvFromFlat(${u}, ${c}, index);\n    return sampleTexture(${o}, uv);\n  }\n`}function D8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(t[0]===1){let m=t.slice(1),d=[1,2],f=cc(r,m),h=[\"b\",\"row\",\"col\"];return`\n        ${F$(f,e)}\n        vec4 ${n}(int b, int row, int col) {\n          return ${n}(${lc(h,d)});\n        }\n      `}let i=wt();if(e)return`\n    vec4 ${n}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${o}TexShape[0]) / 2.0), ceil(float(${o}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${o}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${o}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${i.texture2D}(${o}, uv);\n    }\n  `;let p=a[0],u=a[1],c=Math.ceil(t[2]/2),l=c*Math.ceil(t[1]/2);return`\n    vec4 ${n}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${p}, ${u}, ${l}, ${c}, b, row, col);\n      return ${i.texture2D}(${o}, uv);\n    }\n  `}function F8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=t[1]*t[2],a=t[2],{newShape:i,keptDims:p}=y.squeezeShape(t),u=i;if(u.length<t.length){let h=cc(r,u),g=[\"row\",\"col\",\"depth\"];return`\n        ${uc(h,e)}\n        float ${n}(int row, int col, int depth) {\n          return ${n}(${lc(g,p)});\n        }\n      `}if(r.shapeInfo.isUniform)return`\n      float ${n}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${s}, ${a}, 1)));\n        ${pc(r)}\n      }\n    `;let c=r.shapeInfo.texShape,l=c[0],m=c[1],d=r.shapeInfo.flatOffset;if(m===s&&d==null)return e?`\n      float ${n}(int row, int col, int depth) {\n        int stride1 = ${o}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n        float ${n}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${a}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${m}.0, ${l}.0);\n          return sampleTexture(${o}, uv);\n        }\n      `;if(m===a&&d==null)return e?`\n      float ${n}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${o}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n    float ${n}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${l}.0);\n      return sampleTexture(${o}, uv);\n    }\n  `;let f=Mu(o);return e?`\n    float ${n}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${o}Shape[1] * ${o}Shape[2];\n      int stride1 = ${o}Shape[2];\n      int index = row * stride0 + col * stride1 + depth + ${f};\n      vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index);\n      return sampleTexture(${o}, uv);\n    }\n    `:`\n      float ${n}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s} + col * ${a} + depth + ${f};\n        vec2 uv = uvFromFlat(${l}, ${m}, index);\n        return sampleTexture(${o}, uv);\n      }\n  `}function O8(r,e){let t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),n=wt();if(e)return`\n    vec4 ${o}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${t}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${t}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${t}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}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 ${n.texture2D}(${t}, uv);\n    }\n  `;let s=r.shapeInfo.logicalShape,a=s.length,i=r.shapeInfo.texShape,p=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=p[0],c=p[1],l=Math.ceil(s[a-1]/2),m=l*Math.ceil(s[a-2]/2),d=\"int b, int row, int col\",f=`b * ${m} + (row / 2) * ${l} + (col / 2)`;for(let h=2;h<a-1;h++)d=`int b${h}, `+d,m*=s[a-h-1],f=`b${h} * ${m} + `+f;return`\n    vec4 ${o}(${d}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});\n      return ${n.texture2D}(${t}, uv);\n    }\n  `}function P8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=t[3],a=t[2]*s,i=t[1]*a,{newShape:p,keptDims:u}=y.squeezeShape(t);if(p.length<t.length){let b=cc(r,p),C=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${uc(b,e)}\n      float ${n}(int row, int col, int depth, int depth2) {\n        return ${n}(${lc(C,u)});\n      }\n    `}if(r.shapeInfo.isUniform)return`\n      float ${n}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${i}, ${a}, ${s}, 1)));\n        ${pc(r)}\n      }\n    `;let c=r.shapeInfo.flatOffset,l=r.shapeInfo.texShape,m=l[0],d=l[1],f=`int stride2 = ${o}Shape[3];`,h=`int stride1 = ${o}Shape[2] * stride2;`,g=`int stride0 = ${o}Shape[1] * stride1;`;if(d===i&&c==null)return e?`\n      float ${n}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${h}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n      float ${n}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${a}, ${s}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${d}.0, ${m}.0);\n        return sampleTexture(${o}, uv);\n      }\n    `;if(d===s&&c==null)return e?`\n      float ${n}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${o}Shape[1] * ${o}Shape[2], ${o}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n      float ${n}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${t[1]*t[2]}, ${t[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${d}.0, ${m}.0);\n        return sampleTexture(${o}, uv);\n      }\n    `;let x=Mu(o);return e?`\n    float ${n}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${h}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index + ${x});\n      return sampleTexture(${o}, uv);\n    }\n  `:`\n    float ${n}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${i} + col * ${a} +\n          depth * ${s} + depth2;\n      vec2 uv = uvFromFlat(${m}, ${d}, index + ${x});\n      return sampleTexture(${o}, uv);\n    }\n  `}function M8(r){let e=r.shapeInfo.logicalShape,t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:p,keptDims:u}=y.squeezeShape(e);if(p.length<e.length){let h=cc(r,p),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${uc(h)}\n      float ${o}(int row, int col, int depth, int depth2, int depth3) {\n        return ${o}(${lc(g,u)});\n      }\n    `}if(r.shapeInfo.isUniform)return`\n      float ${o}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${i}, ${a}, ${s}, ${n})) +\n          depth3;\n        ${pc(r)}\n      }\n    `;let c=r.shapeInfo.flatOffset,l=r.shapeInfo.texShape,m=l[0],d=l[1];if(d===i&&c==null)return`\n      float ${o}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${a}, ${s}, ${n}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${d}.0, ${m}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `;if(d===n&&c==null)return`\n      float ${o}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${e[1]*e[2]*e[3]},\n               ${e[2]*e[3]}, ${e[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${d}.0, ${m}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `;let f=Mu(t);return`\n    float ${o}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${i} + col * ${a} + depth * ${s} +\n          depth2 * ${n} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${m}, ${d}, index);\n      return sampleTexture(${t}, uv);\n    }\n  `}function L8(r){let e=r.shapeInfo.logicalShape,t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=y.squeezeShape(e);if(n.length<e.length){let g=cc(r,n),x=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${uc(g)}\n      float ${o}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${o}(${lc(x,s)});\n      }\n    `}let a=e[5],i=e[4]*a,p=e[3]*i,u=e[2]*p,c=e[1]*u;if(r.shapeInfo.isUniform)return`\n      float ${o}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${u}, ${p}, ${i})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${a}, 1)));\n        ${pc(r)}\n      }\n    `;let l=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,d=m[0],f=m[1];if(f===c&&l==null)return`\n      float ${o}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${u}, ${p}, ${i}, ${a})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${d}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `;if(f===a&&l==null)return`\n      float ${o}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${e[1]*e[2]*e[3]*e[4]},\n               ${e[2]*e[3]*e[4]},\n               ${e[3]*e[4]},\n               ${e[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${d}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `;let h=Mu(t);return`\n    float ${o}(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 * ${u} + depth * ${p} +\n          depth2 * ${i} + depth3 * ${a} + depth4 + ${h};\n      vec2 uv = uvFromFlat(${d}, ${f}, index);\n      return sampleTexture(${t}, uv);\n    }\n  `}function pc(r){let e=r.name,t=y.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`\n    for (int i = 0; i < ${t}; i++) {\n      if (i == index) {\n        return ${e}[i];\n      }\n    }\n  `}function B8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n=\"get\"+o+\"AtOutCoords\",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=R$(r.shapeInfo.logicalShape,e.logicalShape),p=$e(a),u=a-s,c,l=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];s===0?c=\"\":a<2&&i.length>=1?c=\"coords = 0;\":c=i.map(b=>`coords.${l[b+u]} = 0;`).join(`\n`);let m=\"\";a<2&&s>0?m=\"coords\":m=r.shapeInfo.logicalShape.map((b,C)=>`coords.${l[C+u]}`).join(\", \");let d=\"return outputValue;\",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)d=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(h&&!x)a===1?d=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:d=`\n        return vec4(outputValue.x);\n      `;else if(i.length){let b=s-2,C=s-1;i.indexOf(b)>-1&&i.indexOf(C)>-1?d=\"return vec4(outputValue.x);\":i.indexOf(b)>-1?d=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":i.indexOf(C)>-1&&(d=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n    vec4 ${n}() {\n      ${p} coords = getOutputCoords();\n      ${c}\n      vec4 outputValue = get${o}(${m});\n      ${d}\n    }\n  `}function V8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n=\"get\"+o+\"AtOutCoords\",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,p=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===p&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`\n      float ${n}() {\n        return sampleTexture(${t}, resultUV);\n      }\n    `;let u=$e(p),c=R$(r.shapeInfo.logicalShape,e.logicalShape),l=p-i,m,d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];i===0?m=\"\":p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(h=>`coords.${d[h+l]} = 0;`).join(`\n`);let f=\"\";return p<2&&i>0?f=\"coords\":f=r.shapeInfo.logicalShape.map((h,g)=>`coords.${d[g+l]}`).join(\", \"),`\n    float ${n}() {\n      ${u} coords = getOutputCoords();\n      ${m}\n      return get${o}(${f});\n    }\n  `}function $e(r){if(r<=1)return\"int\";if(r===2)return\"ivec2\";if(r===3)return\"ivec3\";if(r===4)return\"ivec4\";if(r===5)return\"ivec5\";if(r===6)return\"ivec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Of(r,e,t){let{newShape:o,keptDims:n}=y.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):o,p=!r&&s>1&&!y.arraysEqual(e,t)&&o.length<s||a;return{useSqueezeShape:p,uniformShape:p?i:e,keptDims:n}}function cc(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function lc(r,e){return e.map(t=>r[t]).join(\", \")}function M$(r,e,t,o){let n=t.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:e.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=D$(n,a,e),p=jS(r.gl,i),u=r.createProgram(p);return O().get(\"ENGINE_COMPILE_ONLY\")?{program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},mw(r,e,u))}function mw(r,e,t){let o={},n={},s={},a=[],i,p,u,c=null,l=null;l=r.getUniformLocation(t,\"NAN\",!1),O().getNumber(\"WEBGL_VERSION\")===1&&(c=r.getUniformLocation(t,\"INFINITY\",!1));let m=!1;for(let d=0;d<e.variableNames.length;d++){let f=e.variableNames[d];o[f]=r.getUniformLocation(t,f,m),o[`offset${f}`]=r.getUniformLocation(t,`offset${f}`,m),e.enableShapeUniforms&&(n[`${f}Shape`]=r.getUniformLocation(t,`${f}Shape`,m),s[`${f}TexShape`]=r.getUniformLocation(t,`${f}TexShape`,m))}return e.enableShapeUniforms&&(i=r.getUniformLocation(t,\"outShape\",m),u=r.getUniformLocation(t,\"outShapeStrides\",m),p=r.getUniformLocation(t,\"outTexShape\",m)),e.customUniforms&&e.customUniforms.forEach((d,f)=>{a[f]=r.getUniformLocation(t,d.name,m)}),{uniformLocations:o,customUniformLocations:a,infLoc:c,nanLoc:l,inShapesLocations:n,inTexShapesLocations:s,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:p}}function P$(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,o)=>{let n=t.logicalShape,s=e[o],a=s.shape;if(!y.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,p=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(i,p))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${p} must match`)})}function L$(r,e,t,o,n){e.program.enableShapeUniforms||(P$(e.inShapeInfos,t),P$([e.outShapeInfo],[o]));let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,a[0],a[1]):r.setOutputMatrixTexture(s.texture,a[0],a[1]),r.setProgram(e.webGLProgram),O().getNumber(\"WEBGL_VERSION\")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((p,u)=>{let c=e.program.variableNames[u],l=e.uniformLocations[c],m=e.uniformLocations[`offset${c}`],d=e.inShapesLocations[`${c}Shape`],f=e.inTexShapesLocations[`${c}TexShape`];if(d){let{uniformShape:h}=Of(e.program.packedInputs,p.shape,p.texData.texShape);switch(h.length){case 1:r.gl.uniform1iv(d,new Int32Array(h));break;case 2:r.gl.uniform2iv(d,new Int32Array(h));break;case 3:r.gl.uniform3iv(d,new Int32Array(h));break;case 4:r.gl.uniform4iv(d,new Int32Array(h));break;default:break}}if(f&&r.gl.uniform2i(f,p.texData.texShape[0],p.texData.texShape[1]),l!=null){if(p.isUniform){if(y.sizeFromShape(p.shape)<2)r.gl.uniform1f(l,p.uniformValues[0]);else{let h=p.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),r.gl.uniform1fv(l,h)}return}p.texData.slice!=null&&m!=null&&r.gl.uniform1i(m,p.texData.slice.flatOffset),r.setInputMatrixTexture(p.texData.texture.texture,l,u)}});let i=e.outShapeLocation;if(i)switch(o.shape.length){case 1:r.gl.uniform1iv(i,new Int32Array(o.shape));break;case 2:r.gl.uniform2iv(i,new Int32Array(o.shape));break;case 3:r.gl.uniform3iv(i,new Int32Array(o.shape));break;case 4:r.gl.uniform4iv(i,new Int32Array(o.shape));break;default:break}if(e.outShapeStridesLocation){let p=y.computeStrides(o.shape);switch(o.shape.length){case 2:r.gl.uniform1iv(e.outShapeStridesLocation,new Int32Array(p));break;case 3:r.gl.uniform2iv(e.outShapeStridesLocation,new Int32Array(p));break;case 4:r.gl.uniform3iv(e.outShapeStridesLocation,new Int32Array(p));break;default:break}}e.outTexShapeLocation&&r.gl.uniform2i(e.outTexShapeLocation,o.texData.texShape[0],o.texData.texShape[1]),e.program.customUniforms&&n&&e.program.customUniforms.forEach((p,u)=>{let c=e.customUniformLocations[u],l=n[u];if(p.type===\"float\")r.gl.uniform1fv(c,l);else if(p.type===\"vec2\")r.gl.uniform2fv(c,l);else if(p.type===\"vec3\")r.gl.uniform3fv(c,l);else if(p.type===\"vec4\")r.gl.uniform4fv(c,l);else if(p.type===\"int\")r.gl.uniform1iv(c,l);else if(p.type===\"ivec2\")r.gl.uniform2iv(c,l);else if(p.type===\"ivec3\")r.gl.uniform3iv(c,l);else if(p.type===\"ivec4\")r.gl.uniform4iv(c,l);else throw Error(`uniform type ${p.type} is not supported yet.`)}),r.executeProgram()}function B$(r,e,t){let o=\"\";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Of(r.packedInputs,a.shape,p),m=\"\",d=\"\",f=\"\";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=S.getBroadcastDims(a.shape,t.shape),C=!r.packedInputs&&h===t.shape.length&&y.arraysEqual(p,t.texData.texShape),w=r.packedInputs||c.length>2?\"\":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${w}_${i}`}else{let p=a.isUniform?\"uniform\":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+=\"_\"+o+\"_\"+n+`${O().getNumber(\"WEBGL_VERSION\")}`,s}function lt(r){return O().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Pf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Li.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Pu([\"r\",\"c\",\"d\"],e):ps([\"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    `}};var Mf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Li.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Pu([\"r\",\"c\",\"d\"],e):ps([\"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    `}};var Lf=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=ur.DOWNLOAD;let t=wt();this.outputShape=e,this.userCode=`\n      ${Ff}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}};var Bf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ur.DOWNLOAD;let t=wt();this.outputShape=e,this.userCode=`\n      ${Ff}\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    `}};var U8={R:0,G:1,B:2,A:3},Tl=class{constructor(e,t=!1,o=\"RGBA\"){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\");let a=\"\";for(let i=0;i<o.length;i++){let p=o[i];a+=`\n          if(offset == ${i}) {\n            result = values[${U8[p]}];\n          }`}this.userCode=`\n      ${this.enableShapeUniforms?ac():sc(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int flatIndex = getFlatIndex(coords);\n        float result = 0.;\n        int offset = imod(flatIndex, ${o.length});\n\n        flatIndex = idiv(flatIndex, ${o.length}, 1.);\n\n        int r = flatIndex / texShape[1];\n        if (r < texShape[0]) {\n          int c = imod(flatIndex, texShape[1]);\n          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n          vec4 values = ${n.texture2D}(A, uv);\n          ${a}\n        }\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}};var Vf=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let o=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let n=\"\",s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let p=a*2+i;n+=`\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 = ${o.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${p}] = values[0];\n            } else if (offset == 1) {\n              result[${p}] = values[1];\n            } else if (offset == 2) {\n              result[${p}] = values[2];\n            } else {\n              result[${p}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?ac():sc(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          ${n}\n\n          ${o.output} = ${s};\n        }\n    `}};var $w={};Ge($w,{bindVertexProgramAttributeStreams:()=>Sw,createBufferFromOutputTexture:()=>vw,createFloat16MatrixTexture:()=>xw,createFloat16PackedMatrixTexture:()=>Cw,createFloat32MatrixTexture:()=>gw,createIndexBuffer:()=>hw,createPackedMatrixTexture:()=>bw,createUnsignedBytesMatrixTexture:()=>yw,createVertexBuffer:()=>fw,createVertexShader:()=>dw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Nw,downloadFloat32MatrixFromBuffer:()=>kw,downloadMatrixFromPackedOutputTexture:()=>_w,downloadPackedMatrixFromBuffer:()=>Tw,getInternalFormatForFloat16MatrixTexture:()=>Wf,getInternalFormatForFloat16PackedMatrixTexture:()=>Hf,getInternalFormatForFloat32MatrixTexture:()=>zf,getInternalFormatForPackedMatrixTexture:()=>Gf,getInternalFormatForUnsignedBytesMatrixTexture:()=>Uf,uploadDenseMatrixToTexture:()=>ww,uploadPixelDataToTexture:()=>Iw});function dw(r){let e=wt(),t=`${e.version}\n    precision highp float;\n    ${e.attribute} vec3 clipSpacePos;\n    ${e.attribute} vec2 uv;\n    ${e.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;return qS(r,t)}function fw(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return QS(r,e)}function hw(r){let e=new Uint16Array([0,1,2,2,1,3]);return ZS(r,e)}function _l(r,e,t,o,n,s){ew(e,t);let a=JS(r),i=r.TEXTURE_2D;return pe(r,()=>r.bindTexture(i,a)),pe(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),pe(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),pe(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),pe(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),O().getNumber(\"WEBGL_VERSION\")===1?pe(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)):pe(r,()=>r.texStorage2D(i,1,o,e,t)),pe(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:a,texShape:[t,e]}}function zf(r){return r.internalFormatFloat}function gw(r,e,t,o){let[n,s]=Ou(e,t);return _l(r,n,s,zf(o),o.textureFormatFloat,r.FLOAT)}function Wf(r){return r.internalFormatHalfFloat}function xw(r,e,t,o){let[n,s]=Ou(e,t);return _l(r,n,s,Wf(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function Uf(r){return r.downloadTextureFormat}function yw(r,e,t,o){let[n,s]=Ou(e,t);return _l(r,n,s,Uf(o),r.RGBA,r.UNSIGNED_BYTE)}function Gf(r){return r.internalFormatPackedFloat}function bw(r,e,t,o){let[n,s]=Qs(e,t);return _l(r,n,s,Gf(o),r.RGBA,r.FLOAT)}function Hf(r){return r.internalFormatPackedHalfFloat}function Cw(r,e,t,o){let[n,s]=Qs(e,t);return _l(r,n,s,Hf(o),r.RGBA,o.textureTypeHalfFloat)}function Sw(r,e,t){return pe(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Af(r,e,\"clipSpacePos\",t,3,20,0)&&Af(r,e,\"uv\",t,2,20,12)}function ww(r,e,t,o,n,s){pe(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,p;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,p=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,p=s.internalFormatPackedFloat),a.set(n),O().getNumber(\"WEBGL_VERSION\")===2?pe(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t,o,r.RGBA,i,a)):pe(r,()=>r.texImage2D(r.TEXTURE_2D,0,p,t,o,0,r.RGBA,i,a)),pe(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Iw(r,e,t){pe(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?O().getNumber(\"WEBGL_VERSION\")===2?pe(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t.width,t.height,r.RGBA,r.UNSIGNED_BYTE,t.data)):pe(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):O().getNumber(\"WEBGL_VERSION\")===2?pe(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,t)):pe(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),pe(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function vw(r,e,t,o){let n=r.createBuffer();pe(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return pe(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),pe(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),pe(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function kw(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function Nw(r,e,t,o){let[n,s]=Ou(e,t),a=4,i=new Uint8Array(k$(e*t,a));return pe(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function Tw(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(N$(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function _w(r,e,t){let o=new Float32Array(e*t*4);return pe(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var Lu=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=O().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,US(t,e)):this.gl=zr(t),e=this.gl,O().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>pe(s,()=>s.createVertexArray()),this.bindVertexArray=a=>pe(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>pe(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>pe(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>pe(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>pe(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>pe(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>pe(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),O().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=tc(this.gl,s),Wr(this.gl,a))this.textureHalfFloatExtension=tc(this.gl,a);else if(O().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(o),Wr(this.gl,n))this.colorBufferHalfFloatExtension=tc(this.gl,n);else if(O().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(o=\"EXT_color_buffer_float\",Wr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Wr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=fw(this.gl),this.indexBuffer=hw(this.gl),this.framebuffer=tw(this.gl),this.textureConfig=vl(this.gl,this.textureHalfFloatExtension)}get debug(){return O().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;pe(e,()=>e.finish()),pe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),pe(e,()=>e.deleteFramebuffer(this.framebuffer)),pe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),pe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),pe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),gw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),xw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),yw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Iw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),ww(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Cw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),bw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Rf(this.gl,this.framebuffer),this.outputTexture=null),pe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>Nw(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return Tw(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return kw(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=vw(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(O().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>_w(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=dw(t));let o=XS(t);pe(t,()=>t.attachShader(o,this.vertexShader)),pe(t,()=>t.attachShader(o,e)),YS(t,o);let n;return n=Object.assign(o,{vao:this.createVertexArray()}),this.bindVertexArray(n.vao),pe(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(Sw(t,n,this.vertexBuffer),\"gpgpu_util.bindVertexProgramAttributeStreams not fully successful.\"),this.debug&&kl(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(pe(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&kl(this.gl,this.program)),pe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?rw(this.gl,e,t):ow(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),pe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),nw(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Qs(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&kl(this.gl,this.program),rc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}pe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),pe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=tc(this.gl,O().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(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=G8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in O().platform&&(o=O().platform.setTimeoutCustom.bind(O().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Nl(this.gl,e,this.framebuffer),this.debug&&rc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Nl(this.gl,this.outputTexture,this.framebuffer),this.debug&&rc(this.gl)):Rf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Nl(n,e,this.framebuffer),this.debug&&rc(n),this.outputTexture=e,pe(n,()=>n.viewport(0,0,t,o)),pe(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),pe(this.gl,()=>this.gl.scissor(e,t,o,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 G8(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{addImpl:V$,bincountImpl:Kf,bincountReduceImpl:z$,castImpl:W$,ceilImpl:U$,concatImpl:G$,equalImpl:H$,expImpl:K$,expm1Impl:q$,floorImpl:j$,gatherNdImpl:X$,gatherV2Impl:Y$,greaterImpl:Q$,greaterEqualImpl:Z$,lessImpl:J$,lessEqualImpl:eE,linSpaceImpl:tE,logImpl:rE,maxImpl:oE,maximumImpl:nE,minimumImpl:sE,multiplyImpl:aE,negImpl:iE,notEqualImpl:uE,prodImpl:pE,raggedGatherImpl:cE,raggedRangeImpl:lE,raggedTensorToTensorImpl:mE,rangeImpl:dE,rsqrtImpl:fE,scatterImpl:hE,sigmoidImpl:gE,simpleAbsImpl:qf,sliceImpl:xE,sparseFillEmptyRowsImpl:yE,sparseReshapeImpl:bE,sparseSegmentReductionImpl:jf,sqrtImpl:CE,stridedSliceImpl:SE,stringNGramsImpl:wE,stringSplitImpl:IE,stringToHashBucketFastImpl:vE,subImpl:kE,tileImpl:NE,topKImpl:TE,transposeImpl:Bu,uniqueImpl:_E}=Zp;function Ew(r,e){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,e).map(t=>`${r}.${t}`)}function Et(r,e){return e===1?[r]:Ew(r,e)}function $E(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o<r;o++)t+=e[o],o<r-1&&(t+=\",\");return t}var Xf=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=lt(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=Et(\"rc\",this.rank),o=$e(this.rank),n=this.getOutOfBoundsCondition(t),s=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${o} rc = getOutputCoords();\n\n          if(${n}) {\n            setOutput(vec4(0));\n          } else {\n            ${s}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let o=0;o<=1;o++)for(let n=0;n<=1;n++){let s=`${o===0?\"r\":\"rp1\"}, ${n===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o<this.rank;o++)t+=`${e[o]} >= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),o=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],n=this.enableShapeUniforms?`outShape[${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 >= ${o};\n      bool rEdge = rp1 >= ${n};\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]})`}};var mc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n        ${s}\n        ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${n}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${n>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${H8(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?ac():sc(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        ${o}\n\n        setOutput(result);\n      }\n    `}};function H8(r,e){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${e?A$([\"r\",\"c\",\"d\"],\"inputShape\"):ps([\"r\",\"c\",\"d\"],r)}\n      return ivec3(r, c, d);\n    }\n  `}var Yf=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,o){let n=AE(t,o),s=RE(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=EE(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let i;return n===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Zt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=AE(o,n),a=RE(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=EE(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=O().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(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],c=u.indexOf(e);if(c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(c,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 K8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function EE(r,e,t,o,n){let s=q8(e,o),a;if(n){let[p,u]=Qs(r[0],r[1]);a=p*u}else{let[p,u]=Ou(r[0],r[1]);a=p*u}let i=K8(t,s);return a*i}function q8(r,e){switch(r){case Zt.PACKED_2X2_FLOAT32:return Gf(e);case Zt.PACKED_2X2_FLOAT16:return Hf(e);case Zt.UNPACKED_FLOAT32:return zf(e);case Zt.UNPACKED_FLOAT16:return Wf(e);case Zt.PACKED_4X1_UNSIGNED_BYTE:return Uf(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function j8(r){return O().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:r?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function AE(r,e){if(r===ur.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(r===ur.RENDER||r==null)return j8(e);if(r===ur.DOWNLOAD||r===ur.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function RE(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Jt=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=lt(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    `}},zt=\"if (isnan(x)) return x;\",DE=\"return x;\",Aw=\"return abs(x);\";var FE=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",OE=zt+`\n  return (x < 0.0) ? 0.0 : x;\n`,PE=zt+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Zs=\"return x;\",ME=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var BE=\"return x;\",VE=`\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`,zE=`\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`,WE=`\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`,UE=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Er=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}};var Qf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let t=e.length,o=Et(\"rc\",t),n=$e(t),s=$E(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${n} rc = getOutputCoords();\n        vec4 packedInput = getA(${s});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}};var Y8=Vt.whereImpl,Q8=1e-7,Z8=1e-4,Zf={};function J8(r){return r in Zf||(Zf[r]={}),Zf[r]}var eY=O().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),tY=600;function rY(){return O().global.screen==null?1024:O().global.screen.height*O().global.screen.width*window.devicePixelRatio*tY/1024/1024}var Vi=class extends Zr{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!O().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Lu)t=e;else{let o=zr(O().getNumber(\"WEBGL_VERSION\"),e);t=new Lu(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=zr(O().getNumber(\"WEBGL_VERSION\"));t=new Lu(o),this.binaryCache=J8(O().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Yf(this.gpgpu),this.numMBBeforeWarning=rY(),this.texData=new Fo(this,sr())}nextDataId(){return Vi.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=oc(t),c=new Tl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((O().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||O().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"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:o,values:e,usage:ur.UPLOAD,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,o,n,s){if(O().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:o,dtype:n,values:t,usage:ur.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Er(i,Zs):m=new Jt(i,Zs);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=S.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Er(n,Zs):f=new Jt(n,Zs);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(O().getBool(\"DEBUG\")&&!O().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&O().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,c;if(a!==\"complex64\"&&O().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Il(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=S.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;pe(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&sr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Er(s,Zs):d=new Jt(s,Zs);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==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 c=this.decode(e,t.customTexShape),l=sr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let o=e[t];if(!KS(o))throw O().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${o} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:o,isPacked:n}=this.texData.get(e),s=y.sizeFromShape(t);if(O().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let m=this.decode(e),d=this.texData.get(m.dataId),f=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Il(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),f}let a=O().getBool(\"WEBGL_PACK\")&&n===!0,i=a?oc(t):t,p=a?new Bf(i):new Lf(i),u=this.runWebGLProgram(p,[{shape:i,dtype:o,dataId:e}],\"float32\"),c=this.texData.get(u.dataId),l=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),l}timerAvailable(){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${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 O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(O().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:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=eY){return O().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return Y8(e.shape,t)}packedUnaryOp(e,t,o){let n=new Er(e.shape,t),s=this.compileAndRun(n,[e],o);return sr().makeTensorFromTensorInfo(s)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let n=qf(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,Aw,e.dtype);let t=new Jt(e.shape,Aw),o=this.compileAndRun(t,[e]);return sr().makeTensorFromTensorInfo(o)}makeTensorInfo(e,t,o){let n;if(t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return sr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new Qf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xf(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Ua(e.shape),...Ga(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Ua(t),...Ga(t)],a=new mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=oc(s),p;n?p=new Mf(i):p=new Pf(i);let u=!0,c=[t!=null?t:Il(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Li.DENSE){let x=a!=null?a:Il(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.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(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=O().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Bi(b.shape,x.shape)){let C=x,w=x.shape;x.shape=b.shape,x=this.packedReshape(x,w),u.push(x),b=this.texData.get(x.dataId),C.shape=w}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=B$(e,c,l),d=this.getAndSaveBinary(m,()=>M$(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),O().get(\"ENGINE_COMPILE_ONLY\")||L$(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=O().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!O().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(O().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=Ee(()=>{if(!O().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=O().getBool(\"DEBUG\");O().set(\"DEBUG\",!1);let t=this.abs(Ce(1e-8)).dataSync()[0];if(O().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Q8:Z8}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=sw(o,p),t.texShape=l),s!=null){let m=oc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Qs(l[0],l[1])),p?d=new Vf(m,g):d=new Tl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=ur.PIXELS:C.usage=ur.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let w=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,w,k),E=this.texData.get(_.dataId);t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,O().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=E.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=oY(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await AC(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Ef(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:p,outShapeStridesLocation:u,outTexShapeLocation:c}=mw(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=p,e.outShapeStridesLocation=u,e.outTexShapeLocation=c}}createTensorFromGPUData(e,t,o){e.channels=e.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=e,p=sr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return sr().makeTensorFromDataId(u,t,o,p)}};Vi.nextDataId=0;function oY(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;o<t.length;++o)t[o]=Math.round(r[o]);return t}else throw new Error(`Unknown dtype ${e}`)}var nY=\"4.2.0\";function GE(){O().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}ki.isBrowser()&&Ti(\"webgl\",()=>new Vi,2);var CJe={forceHalfFloat:GE};var dc=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;var io=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=lt(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    `}};var Js=`\n  result.r = isNaN.r ? NAN : result.r;\n  result.g = isNaN.g ? NAN : result.g;\n  result.b = isNaN.b ? NAN : result.b;\n  result.a = isNaN.a ? NAN : result.a;\n`;var _o=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=lt(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${$e(s)} coords = getOutputCoords();\n        `,s===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) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let p=Et(\"coords\",s);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${p[s-2]} + 1) >= outShape[${s} - 2];\n            bool nextColOutOfBounds =\n              (${p[s-1]} + 1) >= outShape[${s} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n            bool nextColOutOfBounds =\n              (${p[s-1]} + 1) >= ${this.outputShape[s-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 At(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var HE={kernelName:mo,backendName:\"webgl\",kernelFunc:At};function Ar(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var KE={kernelName:ri,backendName:\"webgl\",kernelFunc:Ar};var Rw=\"return (a < 0.) ? b * a : a;\",Dw=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function sY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new _o(Dw,n.shape,a.shape):new io(Rw,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var qE={kernelName:ln,backendName:\"webgl\",kernelFunc:sY};var Fw=\"return (a < 0.) ? b * a : a;\",Ow=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function aY(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new _o(Ow,o.shape,n.shape):new io(Fw,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var jE={kernelName:Rn,backendName:\"webgl\",kernelFunc:aY};var $o=\"if (isnan(x)) return x;\";function he({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Er(a.shape,e):c=new Jt(a.shape,r),i.runWebGLProgram(c,[a],p)}}function tt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[w,k]=C,_={dataId:w.dataId,dtype:w.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},A=new io(r,p.shape,u.shape);return c.runWebGLProgram(A,[_,E],dt(w.dtype,k.dtype))}),b=Ar({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?S.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),w=c.makeTensorInfo(C,l),k=c.texData.get(w.dataId);return k.values=b,w}let m=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new _o(e,p.shape,u.shape,t):d=new io(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Ha(r,e=!1){if(r===\"linear\")return e?BE:DE;if(r===\"relu\")return e?zE:OE;if(r===\"elu\")return e?VE:FE;if(r===\"relu6\")return e?WE:PE;if(r===\"prelu\")return e?Ow:Fw;if(r===\"leakyrelu\")return e?Dw:Rw;if(r===\"sigmoid\")return e?UE:ME;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var fc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=lt(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:u?g=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:g=`vec4 activation(vec4 x) {\n          ${i}\n        }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",w=\"rc.x\";e[0]<t[0]?C=`imod(rc.x, ${e[0]})`:t[0]<e[0]&&(w=`imod(rc.x, ${t[0]})`),this.userCode=`\n      ${g}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${l}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        int batchA = ${C};\n        int batchB = ${w};\n        for (int i = 0; i < ${l}; i++) {\n          vec4 a = getMatrixA(batchA, ${m});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${f[0]} * ${h[0]});\n          result += (${f[1]} * ${h[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${x}\n\n        setOutput(result);\n      }\n    `}};var Pw={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},$l=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),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    `}};var XE=\"return a * b;\";function El(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=S.upcastType(o.dtype,n.dtype);if(o.dtype===\"complex64\"){let i=t.texData.get(o.dataId),p=t.texData.get(n.dataId),u=new $l(Pw.REAL,o.shape,n.shape),c=new $l(Pw.IMAG,o.shape,n.shape),l=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:n.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Ar({inputs:{real:m,imag:d},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),p=t.texData.get(n.dataId),[u,c]=aE(o.shape,n.shape,i.values,p.values,s),l=t.makeTensorInfo(c,s),m=t.texData.get(l.dataId);return m.values=u,l}let a;return O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?a=new _o(XE,o.shape,n.shape):a=new io(XE,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var YE={kernelName:kn,backendName:\"webgl\",kernelFunc:El};function QE(r,e,t){let o=[Ua(r.shape),...Ga(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Ua(e),...Ga(e)],a=new mc(s,o),i=!0,p=[o],u=t.runWebGLProgram(a,[n],r.dtype,p,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}function re(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),p=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(p);y.assert(i===u,()=>`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Bi(n.shape,p)&&!(c.texture!==null&&Bi(c.shape,p))?QE(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ZE={kernelName:_s,backendName:\"webgl\",kernelFunc:re};var Al=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n        if (inIdx < 0 || inIdx >= ${s}) {\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        ${c}\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 * ${o};\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 + ${i};\n        if (${p===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${p===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${p===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}};var Jf=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${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 c=Math.floor(o/4)*4,l=o%4,m=`\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 = ${p}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${p}(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\",m=`\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\",m=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n        if (inIdx < 0 || inIdx >= ${s}) {\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        ${f}\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 * ${o};\n\n        vec4 minMaxValue = vec4(${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 < ${c}; 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          ${m}\n        }\n\n        int inIdx = inOffset + ${c};\n        if (${l===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${m}\n        } else if (${l===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${m}\n        } else if (${l===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${m}\n        }\n        setOutput(${u});\n      }\n    `}};function uY(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Ur(r,e,t,o){let n=uY(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:p,outSize:u}=n[a],c,l;t===\"mean\"?c=a===0?new Al({windowSize:p,inSize:i,batchSize:r.shape[0],outSize:u},i):new Al({windowSize:p,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Jf({windowSize:p,inSize:i,batchSize:r.shape[0],outSize:u},t),l=s,s=o.runWebGLProgram(c,[s],e),l.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(l)}return s}var eh=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=$e(this.rank),s=pY(t);this.userCode=`\n    void main() {\n      ${n} resRC = getOutputCoords();\n      setOutput(getA(${s}));\n    }\n    `}};function pY(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var th=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=$e(this.rank),s=Ew(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,p=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${n} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${p}) {\n        result[1] = ${u};\n      }\n      --${s[this.rank-1]};\n      if(++${s[this.rank-2]} < ${o[this.rank-2]}) {\n        result[2] = ${u};\n        if(${p}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function zi(r,e,t){let o=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new th(r.shape,e):new eh(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function JE(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,p=S.getAxesPermutation(i,s),u=p!=null,c=r;u&&(c=zi(r,p,o),i=S.getInnerMostAxes(i.length,s)),S.assertAxesAreInnerMostDims(\"sum\",i,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,i),d=l;t&&(d=S.expandShapeToKeepDim(l,a));let f=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/f,x=re({inputs:{x:c},attrs:{shape:[g,f]},backend:o}),b=Ta(r.dtype),C=Ur(x,b,\"sum\",o),w=re({inputs:{x:C},attrs:{shape:d},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(C),u&&o.disposeIntermediateTensorInfo(c),w}function Vu(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return JE(n,s,a,t)}var eA={kernelName:Kn,backendName:\"webgl\",kernelFunc:Vu};function yt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,p=new Array(i);for(let c=0;c<p.length;c++)p[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let l=a.texData.get(n.dataId).values,m=Bu(l,n.shape,n.dtype,s,p);u=a.makeTensorInfo(p,n.dtype);let d=a.texData.get(u.dataId);d.values=m}else u=zi(n,s,a);return u}var tA={kernelName:ro,backendName:\"webgl\",kernelFunc:yt};var Mw=1e3;function zu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=yr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=re({inputs:{x:r},backend:n,attrs:{shape:k}}),A=re({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[E,A],D=Math.max(x,b),P=t?E.shape[1]:E.shape[2],M=s!=null,L=a!=null,V=p===\"leakyrelu\",z=p!=null?Ha(p,!0):null,U=M||L||V||z!=null,K;if((d===1||f===1)&&P>Mw&&U===!1){let q=E,Z=A;t&&(q=yt({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),R.push(q)),o&&(Z=yt({inputs:{x:A},backend:n,attrs:{perm:[0,2,1]}}),R.push(Z));let ee=f!==1,oe=f===1,J=q;ee&&(J=re({inputs:{x:q},backend:n,attrs:{shape:[D,P,1]}}),R.push(J));let te=f===1?2:1,ie=Z;oe&&(ie=re({inputs:{x:Z},backend:n,attrs:{shape:[D,1,P]}}),R.push(ie));let ce=El({inputs:{a:J,b:ie},backend:n});K=Vu({inputs:{x:ce},backend:n,attrs:{axis:te,keepDims:!0}}),R.push(ce)}else{let q=dt(r.dtype,e.dtype),Z=new fc(k,_,[D,d,f],t,o,M,z,L,V),ee=[E,A];if(s!=null&&ee.push(s),L&&ee.push(a),V){let oe=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));ee.push(oe),R.push(oe)}K=n.runWebGLProgram(Z,ee,q)}let H=re({inputs:{x:K},backend:n,attrs:{shape:w}});R.push(K);for(let q of R)n.disposeIntermediateTensorInfo(q);return H}function cY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return 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nh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});if(o.length>O().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=nh({inputs:o.slice(0,p),backend:t}),c=nh({inputs:o.slice(p),backend:t});return nh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=O().getBool(\"WEBGL_PACK\")?new oh(o[0].shape,s):new rh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Po,backendName:\"webgl\",kernelFunc:nh};function xY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=yt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=re({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Ur(h,h.dtype,\"all\",t),x;if(a){let 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t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var lA={kernelName:Lo,backendName:\"webgl\",kernelFunc:yY};var sh=class{constructor(e,t,o){this.variableNames=[\"A\"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,a];let i=t===\"max\"?\">\":\"<\",p=o?\"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 * ${n};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${n}; i++) {\n          int inIdx = ${p};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}};var ah=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=$e(p),c=Et(\"coords\",p),l,m;if(a===1){m=p+1;let A=$e(m);l=`\n        ${A} sourceLocR = ${A}(${c.join()}, 0);\n        ++${c[p-1]};\n        ${A} sourceLocG = ${A}(${c.join()}, 0);\n        ++${c[p-2]};\n        ${A} sourceLocA = ${A}(${c.join()}, 0);\n        --${c[p-1]};\n        ${A} sourceLocB = ${A}(${c.join()}, 0);\n        --${c[p-2]};`}else m=p,l=`\n        ${u} sourceLocR = coords;\n        ++${c[p-1]};\n        ${u} sourceLocG = coords;\n        ++${c[p-2]};\n        ${u} sourceLocA = coords;\n        --${c[p-1]};\n        ${u} sourceLocB = coords;\n        --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(A=>\"int \"+A),g=Et(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=Et(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=Et(\"sourceLocB\",m-1).concat(\"inIdx.b\"),C=Et(\"sourceLocA\",m-1).concat(\"inIdx.a\"),w=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${x.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${C.join()})));`,_=`vec4(\n            getAChannel(${g.join()}),\n            hasNextCol ? getAChannel(${x.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,E=n?\"\":`\n      float getBestIndicesAChannel(${h.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${h.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${E}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${c[p-1]} < ${i[p-1]-1};\n        bool hasNextRow = ${c[p-2]} < ${i[p-2]-1};\n        ${l}\n        ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n          sourceLocB${f}, sourceLocA${f}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${_};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${k}\n          vec4 candidate = ${_};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${w}(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 mA(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new sh(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,\"int32\");if(c.shape[1]===1)return c;let l=mA(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function dA(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=S.computeOptimalWindowSize(s),i=new ah(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,\"int32\");if(u.shape.length===e.shape.length){let c=dA(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function ih(r,e,t,o){let n=[t];if(S.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!O().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=S.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=re({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=mA(r,m,o);s.push(d);let f=re({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return dA(r,e,o)}function bY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var fA={kernelName:Bo,backendName:\"webgl\",kernelFunc:bY};function CY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var hA={kernelName:ei,backendName:\"webgl\",kernelFunc:CY};var SY=zt+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,wY=he({opSnippet:SY}),gA={kernelName:ua,backendName:\"webgl\",kernelFunc:wY};var IY=zt+\"return log(x + sqrt(x * x + 1.0));\",vY=he({opSnippet:IY}),xA={kernelName:pa,backendName:\"webgl\",kernelFunc:vY};var kY=zt+`\n  return atan(x);\n`,NY=he({opSnippet:kY}),yA={kernelName:ca,backendName:\"webgl\",kernelFunc:NY};var TY=dc+`\n  return atan(a, b);\n`,_Y=`\n  vec4 result = atan(a, b);\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  `+Js+`\n  return result;\n`,$Y=tt({opSnippet:TY,packedOpSnippet:_Y}),bA={kernelName:ma,backendName:\"webgl\",kernelFunc:$Y};var EY=zt+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,AY=he({opSnippet:EY}),CA={kernelName:la,backendName:\"webgl\",kernelFunc:AY};var cs=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 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${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 ${A} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let C=\"max\",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(w=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,E=`\n      if (${h}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${C}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${p});\n      const ivec2 pads = ivec2(${d}, ${f});\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 < ${l};\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 < ${k}; wC += 4) {\n            int xC = xCCorner + wC * ${c};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${c}, d),\n              getValue(batch, xR, xC + 2 * ${c}, d),\n              getValue(batch, xR, xC + 3 * ${c}, d)\n            );\n\n            ${E}\n          }\n\n          int xC = xCCorner + ${k};\n          if (${_===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${E}\n          } else if (${_===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${c}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${E}\n          } else if (${_===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${c}, d),\n              getValue(batch, xR, xC + 2 * ${c}, d),\n              initializationValue\n            );\n\n            ${E}\n          }\n        }\n        setOutput(${w});\n      }\n    `}},Wi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",w=\"0.0\";if(C||(w=\"-1.0 / 1e-20\"),o){let D=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${p}, ${u});\n        const ivec3 pads = ivec3(${g}, ${x}, ${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 += ${c}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${f};\n                wR += ${l}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${h};\n                  wC += ${m}) {\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 ${D} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n                      wR * ${h} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let E=Math.floor(a/4)*4,A=a%4,R=`\n      if (${C}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${k}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${p}, ${u});\n      const ivec3 pads = ivec3(${g}, ${x}, ${b});\n      const float initializationValue = ${w};\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 >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${w});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${c}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${f};\n            wR += ${l}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${E}; wC += 4) {\n              int xC = xCCorner + wC * ${m};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${m}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n              );\n\n              ${R}\n            }\n\n            int xC = xCCorner + ${E};\n            if (${A===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${R}\n            } else if (${A===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${m}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${R}\n            } else if (${A===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${m}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n                initializationValue\n              );\n\n              ${R}\n            }\n          }\n        }\n        setOutput(${_});\n      }\n    `}};function RY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;us(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new cs(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var SA={kernelName:Vo,backendName:\"webgl\",kernelFunc:RY};function DY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new Wi(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var wA={kernelName:dp,backendName:\"webgl\",kernelFunc:DY};var uh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n      const ivec2 pads = ivec2(${c}, ${l});\n      const float avgMultiplier = float(${m});\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 < ${p};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${n}.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) / ${s}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},ph=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n      const ivec3 pads = ivec3(${f}, ${h}, ${g});\n      const float avgMultiplier = float(${x});\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 < ${l};\n            wD += ${p}) {\n          float dyD = float(dyDCorner + wD) / ${s}.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 < ${m};\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 += ${c}) {\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 FY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new ph(m);return t.runWebGLProgram(d,[n],a.dtype)}var IA={kernelName:vm,backendName:\"webgl\",kernelFunc:FY};function OY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;us([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new uh(c);return t.runWebGLProgram(l,[n],a.dtype)}var vA={kernelName:mp,backendName:\"webgl\",kernelFunc:OY};function PY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return zu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var kA={kernelName:zo,backendName:\"webgl\",kernelFunc:PY};var ch=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${i};\n        float scale = ${p};\n        float inv = scale * inversesqrt(variance + float(${a}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `}};var lh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 offset = ${i};\n        vec4 scale = ${p};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `}};var MY=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new lh(o.shape,n.shape,s.shape,c,l,p):new ch(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},NA={kernelName:sn,backendName:\"webgl\",kernelFunc:MY};var mh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=$e(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=LY(this.rank),n,s=e.map((a,i)=>`sourceLoc.${Lw[i]} = start[${i}] + coords.${Lw[i]};`);n=`\n        ${t} sourceLoc;\n        ${t} coords = getOutputCoords();\n        ${s.join(`\n`)}\n      `,this.userCode=`\n      void main() {\n        ${n}\n        setOutput(getSource(${o}));\n      }\n    `}},Lw=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function LY(r){if(r===1)return\"sourceLoc\";if(r<=6)return Lw.slice(0,r).map(e=>\"sourceLoc.\"+e).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var dh=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=$e(this.rank),o=Et(\"coords\",this.rank),n=Et(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n      result.x = ${a};\n      if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n        ++${n[this.rank-1]};\n        result.y = ${a};\n        --${n[this.rank-1]};\n      }\n    `,p=this.rank===1?\"\":`\n      --${o[this.rank-1]};\n      if (++${o[this.rank-2]} < ${e[this.rank-2]}) {\n        ++${n[this.rank-2]};\n        result.z = ${a};\n        if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n          ++${n[this.rank-1]};\n          result.w = ${a};\n        }\n      }\n    `,u=this.rank<=4?`sourceLoc = coords +\n            ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n      void main() {\n        ${t} coords = getOutputCoords();\n        ${t} sourceLoc;\n        ${u}\n        vec4 result = vec4(0.);\n        ${i}\n        ${p}\n        setOutput(result);\n      }\n    `}};function BY(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=ut.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function ls(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.texData.get(n.dataId),m=xE(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=ut.isSliceContinous(n.shape,i,p);if(u||!c){let l=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new dh(p):new mh(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),BY(n,i,p,t)}var TA={kernelName:Es,backendName:\"webgl\",kernelFunc:ls};var VY=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=re({inputs:{x:n},backend:t,attrs:{shape:p}}),h=yt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=re({inputs:{x:h},backend:t,attrs:{shape:c}}),x=ls({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},_A={kernelName:bs,backendName:\"webgl\",kernelFunc:VY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=Kf(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var $A={kernelName:ti,backendName:\"webgl\",kernelFunc:zY};function WY(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=S.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return 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a=Ka({inputs:{input:n},backend:t}),i=Vw({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=W$(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return DA(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=Bw({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var FA={kernelName:co,backendName:\"webgl\",kernelFunc:Vw};var OA=\"return ceil(x);\",HY=he({opSnippet:OA,packedOpSnippet:OA,cpuKernelImpl:U$}),PA={kernelName:Wo,backendName:\"webgl\",kernelFunc:HY};var fh=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `}};var hh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `}};function KY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;O().getBool(\"WEBGL_PACK_CLIP\")?i=new hh(n.shape):i=new fh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var MA={kernelName:lo,backendName:\"webgl\",kernelFunc:KY};var gh=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `}};function LA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function qY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new gh(o.shape),a=[LA(o,n.complexTensorInfos.real),LA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var BA={kernelName:hp,backendName:\"webgl\",kernelFunc:qY};var xh=class{constructor(e){this.outputShape=[],this.outputShape=S.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 o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${o.join(`\n        `)}\n      }\n    `}};var bh=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let o=this.outputShape,n=o.length,s=$e(n),a=Et(\"coords\",n),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,n);this.variableNames=e.map((h,g)=>`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h<p.length;h++)p[h]=p[h-1]+e[h][t];let u=i[t],c=i.slice(-2),l=i.join(),m=`if (${u} < ${p[0]}) {\n        return getChannel(\n            getT0(${l}), vec2(${c.join()}));\n        }`;for(let h=1;h<p.length;h++){let g=p[h-1];m+=`\n        if (${u} < ${p[h]}  && ${u} >= ${p[h-1]}) {\n          return getChannel(\n            getT${h}(${yh(i,u,g)}),\n            vec2(${yh(c,u,g)}));\n        }`}let d=p.length,f=p[p.length-1];m+=`\n        return getChannel(\n          getT${d}(${yh(i,u,f)}),\n          vec2(${yh(c,u,f)}));`,this.userCode=`\n      float getValue(${i.map(h=>\"int \"+h)}) {\n        ${m}\n      }\n\n      void main() {\n        ${s} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[n-1]} = ${a[n-1]} + 1;\n        if (${a[n-1]} < ${o[n-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[n-2]} = ${a[n-2]} + 1;\n        if (${a[n-2]} < ${o[n-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[n-1]} = ${a[n-1]} - 1;\n        if (${a[n-2]} < ${o[n-2]} &&\n            ${a[n-1]} < ${o[n-1]}) {\n          result.b = getValue(${a});\n        }\n        setOutput(result);\n      }\n    `}};function yh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Wu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:t})}var VA={kernelName:ci,backendName:\"webgl\",kernelFunc:Wu};function hc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>Ka({inputs:{input:b},backend:t})),f=r.map(b=>Wu({inputs:{input:b},backend:t})),h=hc(d,e,t),g=hc(f,e,t),x=Ar({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(w=>{let _=[-1,y.sizeFromShape(w.shape.slice(e))];return re({inputs:{x:w},backend:t,attrs:{shape:_}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape})),h=S.computeOutShape(d.map(w=>w.shape),1),g=d[0].shape[0]===1,x=G$(f,h,o,g),b=S.computeOutShape(r.map(w=>w.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(w=>t.disposeIntermediateTensorInfo(w)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new Jt(r[0].shape,Zs):new Er(r[0].shape,Zs);return t.runWebGLProgram(d,r,o)}let i=O().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;h<s.length;h+=i){let g=s.slice(h,h+i);d.push(hc(g,e,t))}let f=hc(d,e,t);for(let h of d)t.disposeIntermediateTensorInfo(h);return f}if(a){let d=new bh(s.map(f=>f.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=jY(s,e,t),c=new xh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=re({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function jY(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>re({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function zw(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):hc(p,s,t)}var zA={kernelName:Cs,backendName:\"webgl\",kernelFunc:zw};var gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,w=\"\",k=\"\";o&&(n?w=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${o}\n        }`:s?w=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${o}\n        }`:w=`\n          float activation(float x) {\n            ${o}\n          }\n        `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${w}\n\n      const ivec2 strides = ivec2(${p}, ${u});\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${C}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${x}], coords[${b}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${m}; wR++) {\n          int xR = xRCorner + wR * ${c};\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 < ${f}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${g}) {\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 (${h===1}) {\n\n              if (${g}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${f}) *\n                    getW(wR, wC, ${f}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${f}, xR, xC) *\n                    getW(wR, wC, ${f}, d2);\n              }\n\n            } else if (${h===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${f}, d2),\n                getW(wR, wC, ${f} + 1, d2)\n              );\n\n              if (${g}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${f}),\n                  getX(batch, xR, xC, ${f} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${f}, xR, xC),\n                  getX(batch, ${f} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${h===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${f}, d2),\n                getW(wR, wC, ${f} + 1, d2),\n                getW(wR, wC, ${f} + 2, d2)\n              );\n\n              if (${g}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${f}),\n                  getX(batch, xR, xC, ${f} + 1),\n                  getX(batch, xR, xC, ${f} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${f}, xR, xC),\n                  getX(batch, ${f} + 1, xR, xC),\n                  getX(batch, ${f} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${_}\n        ${k}\n        setOutput(result);\n      }\n    `}},Ch=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${s}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${o}, ${n});\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 < ${l}; wF++) {\n          int xF = xFCorner + wF * ${p};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${m}; 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 * ${c};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${f}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${h===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${f}) *\n                  getW(wF, wR, wC, ${f}, d2);\n              } else if (${h===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${f}),\n                  getX(batch, xF, xR, xC, ${f} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${f}, d2),\n                  getW(wF, wR, wC, ${f} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${h===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${f}),\n                  getX(batch, xF, xR, xC, ${f} + 1),\n                  getX(batch, xF, xR, xC, ${f} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${f}, d2),\n                  getW(wF, wR, wC, ${f} + 1, d2),\n                  getW(wF, wR, wC, ${f} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};var xc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n       int xR; int xC; int xCOffset;\n       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)m+=`\n           vec4 xTexelC${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};`;m+=`\n     for (int r = 0; r < ${u}; r++) {\n      for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {\n       `;for(let g=0;g<c;g++)m+=`\n           xTexelC${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);`;m+=`\n         xR = xRCorner + r * dilations[0];\n         if (xR >=0 && xR < inDims[0]) {\n       `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n           xC = xCCorner + ${x*p};\n           `,i===1){if(x<c&&(a%2===1?(m+=`\n                 xCOffset = xC + 1;\n                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n                   xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${x}.zw = vec2(0.0);\n                   }\n                   xTexelC${x}Ready = 1;\n                 }\n               `,p===1&&x>0?m+=`\n                 xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n                 `:m+=`\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${x} = vec4(previous.zw, xTexelC${x}.xy);\n                   } else {\n                     xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n                   }\n                   `):m+=`\n                 if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n                   xTexelC${x} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${x}.zw = vec2(0.0);\n                   }\n                   xTexelC${x}Ready = 1;\n                 }\n\n                 xC${x} = xTexelC${x};\n                 `,x+1<c)){let b=a%2===0?y.nearestLargerEven(p):p;p%2===0&&a%2===1||p%2!==0&&a%2!==1?(m+=`\n                   xCOffset = xC + imod(pads[1], 2) + ${b};\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n                     xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       xTexelC${x+1}.zw = vec2(0.0);\n                     }\n                     xTexelC${x+1}Ready = 1;\n                   }\n                   `,p>1?m+=`\n                     xCOffset -= 2;\n                     if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                      previous = getX(batch, xR, xCOffset, d1);\n                      xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n                     } else {\n                      xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n                     }\n                     `:m+=`\n                     xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n                     `):b===1?m+=`\n                     xC${x+1} = xTexelC${x};\n                     `:m+=`\n                     xCOffset = xC + ${b};\n\n                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n                       xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n                       if (xCOffset + 1 >= inDims[1]) {\n                         xTexelC${x+1}.zw = vec2(0.0);\n                       }\n                       xTexelC${x+1}Ready = 1;\n                     }\n\n                     xC${x+1} = xTexelC${x+1};\n                     `}}else x<c&&(a%2===1?(m+=`\n                 xCOffset = xC + 1 - strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n                   xTexelC${x} = getX(batch, xR, xCOffset, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${x}.zw = vec2(0.0);\n                   }\n                   xTexelC${x}Ready = 1;\n                 }\n\n                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n                   xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xC + 2 >= inDims[1]) {\n                     xTexelC${x+1}.zw = vec2(0.0);\n                   }\n                   xTexelC${x+1}Ready = 1;\n                 }\n\n                 xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n               `,x+1<c&&(m+=`\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${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n                 `)):(m+=`\n                 if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n                   xTexelC${x} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${x}.zw = vec2(0.0);\n                   }\n                   xTexelC${x}Ready = 1;\n                 }\n\n                 xCOffset = xC + strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n                   xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${x+1}.zw = vec2(0.);\n                   }\n                   xTexelC${x+1}Ready = 1;\n                 }\n\n                 xC${x} = vec4(\n                   xTexelC${x}.xy, xTexelC${x+1}.xy);\n               `,x+1<c&&(m+=`\n                   xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n                 `)));x<c&&(m+=`\n             wTexel = getW(r, ${x}, d1, d2);\n             dotProd += xC${x}.xxzz * vec4(wTexel.xy, wTexel.xy);\n             if(d1 + 1 < ${e.inChannels}) {\n               dotProd += xC${x}.yyww * vec4(wTexel.zw, wTexel.zw);\n             }\n           `,x+1<c&&(m+=`\n               wTexel = getW(r, ${x+1}, d1, d2);\n               dotProd += xC${x+1}.xxzz * vec4(wTexel.xy, wTexel.xy);\n               if(d1 + 1 < ${e.inChannels}) {\n                 dotProd += xC${x+1}.yyww * vec4(wTexel.zw, wTexel.zw);\n               }\n             `))}m+=`\n     }\n   `,m+=`\n     }\n   `,m+=`\n     }\n   `;let d=\"\",f=\"\";o&&(n?d=`vec4 activation(vec4 a) {\n           vec4 b = getPreluActivationWeightsAtOutCoords();\n           ${o}\n         }`:s?d=`vec4 activation(vec4 a) {\n           vec4 b = getLeakyreluAlphaAtOutCoords();\n           ${o}\n         }`:d=`vec4 activation(vec4 x) {\n           ${o}\n         }`,f=\"result = activation(result);\");let h=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n       ${d}\n\n       void main() {\n         ivec4 coords = getOutputCoords();\n         int batch = coords.x;\n         ivec2 xRCCorner = coords.yz * strides - pads;\n         int d2 = coords.w;\n         int xRCorner = xRCCorner.x;\n         int xCCorner = xRCCorner.y;\n\n         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n         vec4 dotProd = vec4(0.000000000000001);\n\n         ${m}\n\n         vec4 result = dotProd - vec4(0.000000000000001);\n         ${h}\n         ${f}\n         setOutput(result);\n       }\n     `}};var Sh=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec4\"},{name:\"pad\",type:\"ivec2\"},{name:\"stride\",type:\"ivec2\"},{name:\"dilation\",type:\"ivec2\"},{name:\"inChannels\",type:\"int\"},{name:\"itemsPerBlockRow\",type:\"int\"},{name:\"outWidth\",type:\"int\"}],this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let{dataFormat:o}=t,n=wt(),s=o===\"channelsLast\",a=s?1:2,i=s?2:3,p=this.enableShapeUniforms?\"if(blockIndex < outShape[2] && pos < outShape[1]) {\":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,u=\"\";for(let c=0;c<=1;c++)for(let l=0;l<=1;l++)u+=`\n          blockIndex = rc.z + ${l};\n          pos = rc.y + ${c};\n\n          ${p}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${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[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${s}) {\n                  innerDims = vec2(d1, ch);\n                  result[${c*2+l}] = getChannel(\n                    getA(rc.x, d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${c*2+l}] = getChannel(\n                    getA(rc.x, ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${u}\n\n        ${n.output} = result;\n      }\n    `}};function wh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Ih({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let w=wh(s.shape,d);w!=null&&(s=re({inputs:{x:s},backend:o,attrs:{shape:w}}),x.push(s))}if(n!=null){let w=wh(n.shape,d);w!=null&&(n=re({inputs:{x:n},backend:o,attrs:{shape:w}}),x.push(n))}if(!((l===1||m===1)&&c>Mw)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let w=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Bi(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=re({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(E);let A=zu({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),R=o.texData.get(A.dataId);y.assert(R.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,R.shape=t.outShape,g=At({inputs:{x:A},backend:o}),g.shape=t.outShape,x.push(A)}else{let w=t.outHeight*t.outWidth,k=re({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,w,t.inChannels]:[t.batchSize,t.inChannels,w]}}),_=re({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),E=zu({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=re({inputs:{x:E},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push(E)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,w=[];if(s!=null){let H=wh(s.shape,f);H!=null&&(s=re({inputs:{x:s},backend:o,attrs:{shape:H}}),w.push(s))}if(n!=null){let H=wh(n.shape,f);H!=null&&(n=re({inputs:{x:n},backend:o,attrs:{shape:H}}),w.push(n))}let k=re({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(k);let _=new Sh(x,t),E=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],A=o.runWebGLProgram(_,[r],\"float32\",E),R=re({inputs:{x:A},backend:o,attrs:{shape:x}});w.push(A),w.push(R);let D=n!=null,P=s!=null,M=i===\"leakyrelu\",L=i?Ha(i,!0):null,V=new fc(f?R.shape:k.shape,f?k.shape:R.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,D,L,P,M),z=f?[R,k]:[k,R];if(n&&z.push(n),P&&z.push(s),M){let H=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(H),w.push(H)}let U=o.runWebGLProgram(V,z,\"float32\"),K=re({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});w.push(U);for(let H of w)o.disposeIntermediateTensorInfo(H);return K}function XY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=Ih({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let h=new xc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))d=vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new gc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=re({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var WA={kernelName:Uo,backendName:\"webgl\",kernelFunc:XY};var kh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=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} - ${n};\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 * ${o} - ${s};\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    `}},Nh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${l}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - 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) / ${n}.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 < ${o}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${s}.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 = ${o} - 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    `}},Th=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=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} - ${s};\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 * ${o} - ${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 * ${n} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},_h=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${p}, ${u}, ${c});\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) / ${s}.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 < ${o}; 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 = ${o} - 1 - wR;\n\n            for (int wC = 0; wC < ${n}; 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 = ${n} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function YY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new kh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var UA={kernelName:oi,backendName:\"webgl\",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=new Nh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var GA={kernelName:Go,backendName:\"webgl\",kernelFunc:QY};function ZY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ch(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var HA={kernelName:gp,backendName:\"webgl\",kernelFunc:ZY};function JY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=S.computeConv3DInfo(n.shape,p,a,1,i),c=new Th(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var KA={kernelName:km,backendName:\"webgl\",kernelFunc:JY};function eQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=S.computeConv3DInfo(p,s.shape,i,1,a),c=new _h(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var qA={kernelName:xp,backendName:\"webgl\",kernelFunc:eQ};var tQ=$o+`\n  return cos(x);\n`,rQ=he({opSnippet:tQ}),jA={kernelName:Ho,backendName:\"webgl\",kernelFunc:rQ};var oQ=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,nQ=he({opSnippet:oQ}),XA={kernelName:Ko,backendName:\"webgl\",kernelFunc:nQ};var $h=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,w,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n      const float height_ratio = float(${g});\n      const float width_ratio = float(${C});\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 = ${x};\n        float width_scale = ${w};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${f} ) {\n          setOutput(float(${s}));\n          return;\n        }\n        float in_x = ${k};\n        if( in_x < 0.0 || in_x > ${h} ) {\n          setOutput(float(${s}));\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    `}};var sQ=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new $h(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},YA={kernelName:Xo,backendName:\"webgl\",kernelFunc:sQ};var Uu;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Uu||(Uu={}));var Rl=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Uu.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${QA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${$e(s)} coords = getOutputCoords();\n        int end = ${ZA(s,\"coords\",this.op)};\n        float val = ${i};\n        int pow2 = int(pow(2.0, index));\n        if (${u}) {\n          int idx = ${c};\n          ${ZA(s,\"coords\",this.op)} = idx;\n          val ${this.op}= getX(${QA(s,\"coords\",this.op)});\n        }\n        setOutput(val);\n      }\n    `}};function QA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function ZA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Eh(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=yt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Rl(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new Rl(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=yt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function aQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Eh(Uu.Prod,n,t,s,a,i)}var JA={kernelName:qo,backendName:\"webgl\",kernelFunc:aQ};function iQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Eh(Uu.Sum,n,t,s,a,i)}var eR={kernelName:jo,backendName:\"webgl\",kernelFunc:iQ};function uQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=Kf(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=z$(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var tR={kernelName:ni,backendName:\"webgl\",kernelFunc:uQ};var Ah=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,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 pQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Ah(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var rR={kernelName:Yo,backendName:\"webgl\",kernelFunc:pQ};var yc=class{constructor(e,t=!1,o=null,n=!1,s=!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=lt(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${o}\n        }`:s?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${o}\n        }`:u=`\n          float activation(float x) {\n            ${o}\n          }\n        `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&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 / ${p};\n        int q = d2 - d1 * ${p};\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        ${l}\n        ${c}\n        setOutput(result);\n      }\n    `}};var bc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x<l;x++)d+=`\n          vec4 xTexelC${x*2};\n          int xTexelC${x*2}Ready;\n          vec4 xTexelC${x*2+1};\n          int xTexelC${x*2+1}Ready;\n          vec4 xC${x};`;d+=`\n    for (int r = 0; r < ${c}; r++) {\n      `;for(let x=0;x<l;x++)d+=`\n          xTexelC${x*2} = vec4(0.0);\n          xTexelC${x*2}Ready = 0;\n          xTexelC${x*2+1} = vec4(0.0);\n          xTexelC${x*2+1}Ready = 0;\n          xC${x} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,p===1){if(b<l&&(i%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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<l)){let C=i%2===0?y.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${C};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${b+1}Ready = 1;\n                  }\n                  `,u>1?d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n                     xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n                    } else {\n                     xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n                    }\n                    `:d+=`\n                    xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                    `):C===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${C};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${b+1};\n                    `}}else b<l&&(i%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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<l&&(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${b+1}.xy);\n              `,b+1<l&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<l&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<l&&(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 f=\"\",h=\"\";o&&(n?f=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${o}\n        }`:s?f=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${o}\n        }`:f=`vec4 activation(vec4 x) {\n          ${o}\n        }`,h=\"result = activation(result);\");let g=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${f}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${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        ${g}\n        ${h}\n        setOutput(result);\n      }\n    `}};function cQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p,dimRoundingMode:u}=o,c=p;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;O().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new bc(l):m=new yc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var oR={kernelName:Qo,backendName:\"webgl\",kernelFunc:cQ};var Rh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=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} - ${n};\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 * ${o} - ${s};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},Dh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${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) / ${n}.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 < ${o}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${s}.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 = ${o} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${p}; dm++) {\n              int d2 = d1 * ${p} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function lQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=S.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Rh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var nR={kernelName:yp,backendName:\"webgl\",kernelFunc:lQ};function mQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Dh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var sR={kernelName:bp,backendName:\"webgl\",kernelFunc:mQ};var Fh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function dQ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=re({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Fh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=re({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var aR={kernelName:si,backendName:\"webgl\",kernelFunc:dQ};var Oh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n      const ivec2 strides = ivec2(${s}, ${a});\n      const ivec2 pads = ivec2(${l}, ${m});\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 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m=re({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var iR={kernelName:ai,backendName:\"webgl\",kernelFunc:fQ};function hQ(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h<l;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:b}=S.getEinsumPermutation(d,p[g]),C;S.isIdentityPermutation(x)?C=s[g]:(C=yt({inputs:{x:s[g]},backend:t,attrs:{perm:x}}),f.push(C));let w=C.shape.slice();for(let k=0;k<b.length;++k)w.splice(b[k],0,1);y.arraysEqual(C.shape,w)||(C=re({inputs:{x:C},backend:t,attrs:{shape:w}}),f.push(C)),m===null?m=C:(m=El({inputs:{a:C,b:m},backend:t}),f.push(m))}h<l-1&&(u[h]>=0&&(m=Vu({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of 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`}};function Mh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=re({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new Dl(\"real\",p,e),c=new Dl(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Ar({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=re({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function EQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!1,t)}var xR={kernelName:ui,backendName:\"webgl\",kernelFunc:EQ};var Lh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `}};function qa(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Lh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var yR={kernelName:ws,backendName:\"webgl\",kernelFunc:qa};var Bh=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${t} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}};var bR={kernelName:rn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bh(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var CR=\"return floor(x);\",AQ=he({opSnippet:CR,packedOpSnippet:CR,cpuKernelImpl:j$}),SR={kernelName:on,backendName:\"webgl\",kernelFunc:AQ};var RQ=`\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`,DQ=`\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`,FQ=tt({opSnippet:RQ,packedOpSnippet:DQ,dtype:\"int32\"}),wR={kernelName:nn,backendName:\"webgl\",kernelFunc:FQ};var Vh=class{constructor(e){this.variableNames=[\"A\"];let t=wt(),[o,n]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n        vec4 values = ${t.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if 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values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${t.output} = result;\n      }\n    `}};var IR={kernelName:Zi,backendName:\"webgl\",kernelFunc:OQ},Cc,Uw=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function OQ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Cc==null||h!==Uw)&&(Uw=h,Cc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Uw})),Cc.canvas.width=p,Cc.canvas.height=u,Cc.drawImage(n,0,0,p,u),n=Cc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=ur.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=O().getBool(\"WEBGL_PACK\")?new zh(l):new Vh(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function PQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,w=i!=null,k=d===\"leakyrelu\",_=()=>{let A=[n,s],R=(D,P)=>{if(P===\"NCHW\"&&D.shape.length===1&&D.shape[0]!==1){let M=re({inputs:{x:D},backend:t,attrs:{shape:[D.shape[0],1,1]}});return b.push(M),M}return D};if(C&&A.push(R(a,c)),w&&A.push(R(i,c)),k){let D=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));A.push(D),b.push(D)}return 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\"))x=Ih({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let A=d?Ha(d,!0):null,R=new xc(g,C,A,w,k),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],P=_();x=t.runWebGLProgram(R,P,\"float32\",D)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))x=vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let A=d?Ha(d,!1):null,R=new gc(g,C,A,w,k),D=_();x=t.runWebGLProgram(R,D,\"float32\")}let E=re({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(A=>t.disposeIntermediateTensorInfo(A)),E}var vR={kernelName:go,backendName:\"webgl\",kernelFunc:PQ};function MQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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out_of_bounds = out_of_bounds || index < 0;\n          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[i]};\n          flattenIndex += index * ${this.strides[i]};`;this.userCode=`\n         void main() {\n          ${s} coords = getOutputCoords();\n          int flattenIndex = 0;\n          bool out_of_bounds = false;\n\n          ${a}\n\n          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n        }\n      `}};function LQ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=S.prepareAndValidate(o,n),m=re({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=re({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=t.readSync(n.dataId),b=t.bufferSync(o),C=X$(x,b,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,C.values)}let f=new Wh(a,l,[u,c],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=re({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var NR={kernelName:an,backendName:\"webgl\",kernelFunc:LQ};var Uh=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=$e(this.rank),n=BQ(e,2);this.userCode=`\n      void main() {\n        ${o} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${n}));\n      }\n    `}};function BQ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n<r.length;n++)n===2?o.push(\"index\"):o.push(`${t[n]}`);return o.join()}function Gw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0];if(O().get(\"DEBUG\")){let b=t.readSync(s.dataId),C=n.shape[p];for(let w=0;w<b.length;++w){let k=b[w];y.assert(k<=C-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=re({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=re({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),w=Y$(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new Uh(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=re({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var TR={kernelName:Is,backendName:\"webgl\",kernelFunc:Gw};var VQ=\"return float(a > b);\",zQ=`\n  return vec4(greaterThan(a, b));\n`,WQ=tt({opSnippet:VQ,packedOpSnippet:zQ,cpuKernelImpl:Q$,dtype:\"bool\"}),_R={kernelName:un,backendName:\"webgl\",kernelFunc:WQ};var UQ=\"return float(a >= b);\",GQ=`\n  return vec4(greaterThanEqual(a, b));\n`,HQ=tt({opSnippet:UQ,packedOpSnippet:GQ,dtype:\"bool\",cpuKernelImpl:Z$}),$R={kernelName:pn,backendName:\"webgl\",kernelFunc:HQ};function KQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!0,t)}var ER={kernelName:pi,backendName:\"webgl\",kernelFunc:KQ};var qQ=\"return float(!isnan(x) && !isinf(x));\",jQ=he({opSnippet:qQ,dtype:\"bool\"}),AR={kernelName:ha,backendName:\"webgl\",kernelFunc:jQ};var XQ=\"return float(isinf(x));\",YQ=he({opSnippet:XQ,dtype:\"bool\"}),RR={kernelName:ga,backendName:\"webgl\",kernelFunc:YQ};var QQ=\"return float(isnan(x));\",ZQ=he({opSnippet:QQ,dtype:\"bool\"}),DR={kernelName:cn,backendName:\"webgl\",kernelFunc:ZQ};var JQ=\"return float(a < b);\",e7=`\n  return vec4(lessThan(a, b));\n`,t7=tt({opSnippet:JQ,packedOpSnippet:e7,cpuKernelImpl:J$,dtype:\"bool\"}),FR={kernelName:mn,backendName:\"webgl\",kernelFunc:t7};var r7=\"return float(a <= b);\",o7=`\n  return vec4(lessThanEqual(a, b));\n`,n7=tt({opSnippet:r7,packedOpSnippet:o7,cpuKernelImpl:eE,dtype:\"bool\"}),OR={kernelName:dn,backendName:\"webgl\",kernelFunc:n7};function s7(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=tE(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var PR={kernelName:li,backendName:\"webgl\",kernelFunc:s7};var a7=$o+`\n  return x < 0.0 ? 0./0. : log(x);\n`,i7=`\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`,u7=he({opSnippet:a7,packedOpSnippet:i7,cpuKernelImpl:rE}),MR={kernelName:fn,backendName:\"webgl\",kernelFunc:u7};var p7=$o+`\n  return log(1.0 + x);\n`,c7=he({opSnippet:p7}),LR={kernelName:xa,backendName:\"webgl\",kernelFunc:c7};var l7=\"return float(a >= 1.0 && b >= 1.0);\",m7=`\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`,d7=tt({opSnippet:l7,packedOpSnippet:m7,dtype:\"bool\"}),BR={kernelName:hn,backendName:\"webgl\",kernelFunc:d7};var f7=\"return float(!(x >= 1.0));\",h7=he({opSnippet:f7}),VR={kernelName:gn,backendName:\"webgl\",kernelFunc:h7};var g7=\"return float(a >= 1.0 || b >= 1.0);\",x7=`\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`,y7=tt({opSnippet:g7,packedOpSnippet:x7,dtype:\"bool\"}),zR={kernelName:xn,backendName:\"webgl\",kernelFunc:y7};var Gh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${a}; j <= ${a}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${i}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${p};\n        setOutput(val);\n      }\n    `}};var Hh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${a};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${a}; j <= ${a}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${p};\n        setOutput(result);\n      }\n    `}};var b7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new Hh(n.shape,s,a,i,p):new Gh(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},WR={kernelName:mi,backendName:\"webgl\",kernelFunc:b7};var Kh=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${n}) * norm + float(${o});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${n})\n                * float(${s})\n                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${s});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}};var C7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Kh(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},UR={kernelName:Tm,backendName:\"webgl\",kernelFunc:C7};function GR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=re({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Ur(i,r.dtype,\"max\",o),u=re({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Hw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,w=new Array(i);for(let E=0;E<w.length;E++)w[E]=n.shape[c[E]];let k=Bu(C,n.shape,n.dtype,c,w);d=t.makeTensorInfo(w,n.dtype);let _=t.texData.get(d.dataId);_.values=k}else d=zi(n,c,t);u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims(\"max\",u,i);let[f,h]=S.computeOutAndReduceShapes(d.shape,u),g=f;a&&(g=S.expandShapeToKeepDim(f,p));let x;if(m){let C=t.texData.get(d.dataId).values,w=oE(C,y.sizeFromShape(h),g,n.dtype);x=t.makeTensorInfo(g,n.dtype);let k=t.texData.get(x.dataId);k.values=w}else x=GR(d,h,g,t);return l&&t.disposeIntermediateTensorInfo(d),x}var HR={kernelName:yn,backendName:\"webgl\",kernelFunc:Hw};var S7=dc+`\n  return max(a, b);\n`,w7=`\n  vec4 result = vec4(max(a, b));\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  `+Js+`\n  return result;\n`,I7=tt({opSnippet:S7,packedOpSnippet:w7,cpuKernelImpl:nE}),KR={kernelName:bn,backendName:\"webgl\",kernelFunc:I7};function v7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;us(n,\"maxPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new cs(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var qR={kernelName:Cn,backendName:\"webgl\",kernelFunc:v7};function k7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Wi(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var jR={kernelName:Cp,backendName:\"webgl\",kernelFunc:k7};var qh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${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 < ${s};\n          wR += ${n}) {\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) / ${o}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(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 * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},jh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n      const ivec3 pads = ivec3(${l}, ${m}, ${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        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${p};\n           wD += ${s}) {\n          float dyD = float(dyDCorner + wD) / ${t}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${u};\n              wR += ${a}) {\n            float dyR = float(dyRCorner + wR) / ${o}.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 < ${c};\n                wC += ${i}) {\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(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${f} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${u} * ${c} +\n                  wR * ${c} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function N7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new Wi(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new jh(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var XR={kernelName:$m,backendName:\"webgl\",kernelFunc:N7};function T7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;us([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=S.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new cs(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new qh(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var YR={kernelName:_m,backendName:\"webgl\",kernelFunc:T7};function QR(r,e,t,o){let n=new cs(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new cs(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var ZR={kernelName:Sp,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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setOutput(value);\n        } else {\n          ${s} coords = outC - start;\n          setOutput(getX(${p}));\n        }\n      }\n    `}};var eg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=$e(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=Et(\"rc\",n),u=Et(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n       if(${c}) {\n      `,n===1?\"\":`}\n       rc = outputLoc;\n       ${p[n-2]} += 1;\n       if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":`  ${p[n-1]} += 1;\n         if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h<g;h++)f+=`\n        ${m[h]}\n        if (${d}) {\n          result[${h}] = float(value);\n        } else {\n          ${s} source = rc - start;\n          result[${h}] = getChannel(getX(${u.join()}), ${l});\n        }\n      `;f+=n===1?\"} \":\"}}\",this.userCode=`\n      const ${s} start = ${s}(${a});\n      const ${s} end = ${s}(${i});\n\n      void main() {\n        ${s} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${f}\n        setOutput(result);\n      }\n    `}};var Xw=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return qa({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new eg(n.shape,s,a):new Jh(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},bD={kernelName:En,backendName:\"webgl\",kernelFunc:Xw};var Y7=`\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,Q7=`\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  bvec4 isNaN1 = lessThan(a, vec4(0.0));\n  bvec4 isNaN2 = lessThan(floor(b), b);\n  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n  `+Js+`\n  return result;\n`,Z7=tt({opSnippet:Y7,packedOpSnippet:Q7}),CD={kernelName:An,backendName:\"webgl\",kernelFunc:Z7};function J7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=S.getAxesPermutation(c,i),m=n;l!=null&&(m=yt({inputs:{x:n},backend:t,attrs:{perm:l}}),c=S.getInnerMostAxes(c.length,i),p.push(m)),S.assertAxesAreInnerMostDims(\"prod\",c,i);let d;if(t.shouldExecuteOnCPU([m])){let f=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=pE(m.shape,m.dtype,f,c);d=t.makeTensorInfo(g,x,h)}else{let[f,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=re({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=Ta(n.dtype),C=Ur(x,b,\"prod\",t);d=re({inputs:{x:C},backend:t,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=S.expandShapeToKeepDim(d.shape,u);d=re({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var SD={kernelName:Dn,backendName:\"webgl\",kernelFunc:J7};function eZ(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=cE(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var wD={kernelName:Ip,backendName:\"webgl\",kernelFunc:eZ};function tZ(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lE(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var ID={kernelName:vp,backendName:\"webgl\",kernelFunc:tZ};function rZ(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=mE(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var vD={kernelName:kp,backendName:\"webgl\",kernelFunc:rZ};var Yw=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=dE(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},kD={kernelName:Ts,backendName:\"webgl\",kernelFunc:Yw};var oZ=\"return 1.0 / x;\",nZ=he({opSnippet:oZ}),ND={kernelName:Fn,backendName:\"webgl\",kernelFunc:nZ};var sZ=zt+`\n  return (x < 0.0) ? 0.0 : x;\n`,aZ=`\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`,iZ=he({opSnippet:sZ,packedOpSnippet:aZ}),TD={kernelName:On,backendName:\"webgl\",kernelFunc:iZ};var uZ=zt+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,pZ=`\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`,cZ=he({opSnippet:uZ,packedOpSnippet:pZ}),_D={kernelName:Ln,backendName:\"webgl\",kernelFunc:cZ};var tg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${c[0]/l[0]},\n          ${c[1]/l[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${p}.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 = ${m};\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    `}};var rg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${c[0]/l[0]},\n          ${c[1]/l[1]},\n          ${c[1]/l[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n                                     ${p}.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 = ${m};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${o-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function lZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new rg(n.shape,p,u,s,a):new tg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var $D={kernelName:Mn,backendName:\"webgl\",kernelFunc:lZ};var og=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${c});\n        const float widthScale = float(${l});\n\n        const float invHeightScale = float(${m});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${f});\n        const int winWidth = int(${h});\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), ${n-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), ${s-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 mZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new og(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var ED={kernelName:Am,backendName:\"webgl\",kernelFunc:mZ};var ng=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${c[0]/l[0]},\n          ${c[1]/l[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${p}.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 + ${m})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}};var sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${c[0]/l[0]},\n          ${c[1]/l[1]},\n          ${c[1]/l[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n                                     ${p}.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 + ${m})));\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 < ${o-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function dZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new sg(n.shape,p,u,s,a):new ng(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var AD={kernelName:Pn,backendName:\"webgl\",kernelFunc:dZ};var ag=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${c});\n        const float widthScale = float(${l});\n\n        const float invHeightScale = float(${m});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${f});\n        const int winWidth = int(${h});\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 >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${p[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${p[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${n}) - 1),\n                ${o} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${s}) - 1),\n                ${o} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n     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ug=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Et(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=$e(o);o===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            ${e[0]} - rc - 1);\n          if(${s}){\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 = ${p(n.slice())};\n          if(${s}){\n            result.g = ${u(n.slice())};\n          }\n          if(${a}) {\n            result.b = ${c(n.slice())};\n            if(${s}) {\n              result.a = ${l(n.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function hZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:t});let p=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new ug(n.shape,i):new ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var DD={kernelName:Bn,backendName:\"webgl\",kernelFunc:hZ};var pg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n        vec3 fill = vec3(${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          ${s}\n          if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n            outputValue = getImage(coords[0], coordY, coordX, 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Sc=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let p=$e(s.length),u=$e(a.length),c=\"\";o===1?c=\"i\":o===2&&(c=\"i, j\");let l=`getIndices(${c})`,m=\"\";n===1?m=\"i\":n===2&&(m=\"i, coords[1]\");let d=`getUpdates(${m})`,f=t>1?\"strides[j]\":\"strides\";this.userCode=`\n        ${p} strides = ${p}(${s});\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 < ${t}; j++) {\n              int index = round(${l});\n              flattenedIndex += index * ${f};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${d};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `}};function CZ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=re({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=re({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new Sc(p,i,d.shape.length,f.shape.length,c,m),x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=re({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var MD={kernelName:Wn,backendName:\"webgl\",kernelFunc:CZ};var cg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) 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lg=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&p.push(`${i[c]}`);n=p.join(),s=u.join()}let a=$e(o);this.userCode=`\n      void main() {\n        ${a} resRC = getOutputCoords();\n        float cVal = getC(${n});\n        if (cVal >= 1.0) {\n          setOutput(getA(${s}));\n        } else {\n          setOutput(getB(${s}));\n        }\n      }\n    `}};function wZ(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new lg(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var BD={kernelName:$s,backendName:\"webgl\",kernelFunc:wZ};var IZ=`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = 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u=[],c=Xw({inputs:{x:n},backend:t,attrs:{paddings:p,constantValue:0}}),l=S.getReshaped(c.shape,s,i,!1),m=S.getPermuted(l.length,s.length,!1),d=S.getReshapedPermuted(c.shape,s,i,!1),f=re({inputs:{x:c},backend:t,attrs:{shape:l}}),h=yt({inputs:{x:f},backend:t,attrs:{perm:m}}),g=re({inputs:{x:h},backend:t,attrs:{shape:d}});return u.push(c),u.push(f),u.push(h),u.forEach(x=>t.disposeIntermediateTensorInfo(x)),g},KD={kernelName:As,backendName:\"webgl\",kernelFunc:PZ};function MZ(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n         ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n         ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n         ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n        ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=yE(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var qD={kernelName:hi,backendName:\"webgl\",kernelFunc:MZ};function LZ(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=bE(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var jD={kernelName:va,backendName:\"webgl\",kernelFunc:LZ};function BZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n              ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n              ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=jf(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var XD={kernelName:gi,backendName:\"webgl\",kernelFunc:BZ};function VZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new 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Sc(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=re({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var QD={kernelName:yi,backendName:\"webgl\",kernelFunc:zZ};function WZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=ls({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var ZD={kernelName:Rs,backendName:\"webgl\",kernelFunc:WZ};var JD=\"return sqrt(x);\",UZ=he({opSnippet:JD,packedOpSnippet:JD,cpuKernelImpl:CE}),eF={kernelName:Hn,backendName:\"webgl\",kernelFunc:UZ};var GZ=\"return x * x;\",HZ=he({opSnippet:GZ}),tF={kernelName:bi,backendName:\"webgl\",kernelFunc:HZ};var rF=\"return (a - b) * (a - b);\",KZ=tt({opSnippet:rF,packedOpSnippet:rF}),oF={kernelName:jn,backendName:\"webgl\",kernelFunc:KZ};function qZ({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=zt+`\n    return x > 0.0 ? 1.0 : float(${e.alpha});\n  `,s=new Jt(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var nF={kernelName:fo,backendName:\"webgl\",kernelFunc:qZ};var mg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=$e(o.length),a=$e(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n      ${s} begin = ${s}(${e});\n      ${s} strides = ${s}(${t});\n\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${i}));\n      }\n    `}};function jZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=re({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let E=ut.computeOutShape(b,C,w),A=ls({inputs:{x:n},backend:t,attrs:{begin:b,size:E}});k=re({inputs:{x:A},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(A)}else if(t.shouldExecuteOnCPU([n])){let A=t.readSync(n.dataId),R=le(n.shape,n.dtype,A),D=SE(d,R,w,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let A=new mg(b,w,d);k=t.runWebGLProgram(A,[n],n.dtype)}let _=re({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),_}var sF={kernelName:Xn,backendName:\"webgl\",kernelFunc:jZ};function XZ(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=wE(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var aF={kernelName:Ds,backendName:\"webgl\",kernelFunc:XZ};function YZ(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;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(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,c,l]=IE(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],\"int32\",u),t.makeTensorInfo([m],\"string\",c),t.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var iF={kernelName:Ci,backendName:\"webgl\",kernelFunc:YZ};function QZ(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=t.readSync(s.dataId),i=vE(a,n);return t.makeTensorInfo(s.shape,\"int32\",i)}var uF={kernelName:Si,backendName:\"webgl\",kernelFunc:QZ};var ZZ=\"return tan(x);\",JZ=he({opSnippet:ZZ}),pF={kernelName:Qn,backendName:\"webgl\",kernelFunc:JZ};var e9=`\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,t9=he({opSnippet:e9}),cF={kernelName:Zn,backendName:\"webgl\",kernelFunc:t9};var dg=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[a]*t[a];this.outputShape=o,this.rank=o.length;let n=$e(this.rank),s=r9(e);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        setOutput(getA(${s}));\n      }\n    `}};function r9(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n<r.length;n++)o.push(`imod(${t[n]}, ${r[n]})`);return o.join()}function Qw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;if(n.dtype===\"string\"||n.shape.length>5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=NE(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new dg(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var lF={kernelName:to,backendName:\"webgl\",kernelFunc:Qw};var fg=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     `}},hg=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 Gu(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function mF(r){let e=1;for(;e<r;)e*=2;return e}function o9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=O().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),p=O().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),u=n.shape,c=u[u.length-1];if(t.shouldExecuteOnCPU([n])||c<i||s>p){let D=t.readSync(n.dataId),[P,M]=TE(D,u,n.dtype,s,a);return[t.makeTensorInfo(P.shape,P.dtype,P.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,qa({attrs:{shape:u,dtype:\"int32\",value:0},backend:t})];let l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=re({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&Gu(t,d);let x=mF(s),b=mF(c),C=null,w=()=>C===null?[g,g]:[g,C],k=(D,P,M)=>{let L=w(),V=new fg(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[D],[P]],K=C;C=t.runWebGLProgram(V,L,\"int32\",U),Gu(t,K)};for(let D=1;D<x;D*=2){let P=D*2;for(let M=D;M>=1;M/=2)k(P,M,[h,b])}for(let D=b;D>x;D/=2){let P=w(),M=new hg([h,D/2]),V=[[c],[C===null?1:0],[x]],z=C;C=t.runWebGLProgram(M,P,\"int32\",V),Gu(t,z);let U=x/2,K=U*2;for(let H=U;H>=1;H/=2)k(K,H,C.shape)}let _=C;C=ls({inputs:{x:C},backend:t,attrs:{begin:0,size:[h,s]}}),Gu(t,_);let E=Gw({inputs:{x:g,indices:C},backend:t,attrs:{axis:1,batchDims:1}});Gu(t,g);let A=u.slice(0,-1);A.push(s),_=C,C=re({inputs:{x:C},attrs:{shape:A},backend:t}),Gu(t,_);let R=E;return E=re({inputs:{x:E},attrs:{shape:A},backend:t}),Gu(t,R),[E,C]}var dF={kernelName:Jn,backendName:\"webgl\",kernelFunc:o9};var gg=class{constructor(e,t,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${p} == 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 (${p} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${p} == 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 < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${s});\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(${s});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${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 n9(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new gg(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],\"float32\")}var fF={kernelName:es,backendName:\"webgl\",kernelFunc:n9};function s9(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;us(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=_E(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var hF={kernelName:Np,backendName:\"webgl\",kernelFunc:s9};function a9(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let l=[],m=new Array(i).fill(0),d=a.shape.slice();d[s]=1;let f=new Array(p);for(let h=0;h<f.length;h++){m[s]=h;let g=ls({inputs:{x:a},backend:t,attrs:{begin:m,size:d}}),x=re({inputs:{x:g},backend:t,attrs:{shape:u}});f[h]=x,l.push(g)}return l.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var gF={kernelName:Fs,backendName:\"webgl\",kernelFunc:a9};var xg=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";s%o>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${s}) {\n          return initializationValue;\n        }\n      `);let f=\"\";s%o>0&&(f=`\n        if (inIdx < 0 || inIdx >= ${s}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${p};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${f}\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          ${a})) * float(${o}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${c}; 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          ${m}\n        }\n\n        int inIdx = inOffset + ${c};\n        if (${l===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          ${m}\n        } else if (${l===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          ${m}\n        } else if (${l===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)) 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b.stringBytes=S.fromStringArrayToUint8(g),d.forEach(C=>t.disposeData(C.dataId)),i}let p=y.sizeFromShape(a[0].shape.slice(0,o)),u=0,c=a.map(d=>{let f=y.sizeFromShape(d.shape.slice(o));return u+=f,f}),l=a.map(d=>t.typedArrayFromHeap(d)),m=t.typedArrayFromHeap(i);for(let d=0;d<p;d++){let f=d*u;for(let h=0;h<l.length;h++){let g=c[h],x=d*g,b=l[h].subarray(x,x+g);m.set(b,f),f+=g}}return i}var GF={kernelName:Cs,backendName:\"wasm\",kernelFunc:Zw};var HF;function D9(r){HF=r.wasm.cwrap(Uo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function F9(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l,dataFormat:m}=t,d=S.convertConv2DDataFormat(m),f=S.computeConv2DInfo(n.shape,s.shape,p,u,c,l,!1,d),h=f.filterHeight,g=f.filterWidth,x=f.padInfo.top,b=f.padInfo.right,C=f.padInfo.bottom,w=f.padInfo.left,k=f.dilationHeight,_=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,R=f.inChannels,D=f.outChannels,P=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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P9(r){let{backend:e,inputs:t,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,inputShape:c}=o,l=1,m=S.convertConv2DDataFormat(p),d=S.computeConv2DInfo(c,s.shape,a,l,i,u,!1,m),{batchSize:f,filterHeight:h,filterWidth:g,inChannels:x,inHeight:b,inWidth:C,outChannels:w,outHeight:k,outWidth:_,strideHeight:E,strideWidth:A}=d,R=h-1-d.padInfo.top,D=g-1-d.padInfo.left,P=d.dataFormat===\"channelsLast\",M=y.computeStrides(d.inShape),L=y.computeStrides(n.shape),[V,z,U]=y.computeStrides(s.shape),K=M[0],H=P?M[1]:M[2],q=P?M[2]:1,Z=P?1:M[1],ee=L[0],oe=P?L[1]:L[2],J=P?L[2]:1,te=P?1:L[1],ie=e.makeOutput(d.inShape,\"float32\"),ce=e.dataIdMap.get(ie.dataId).id,ye=e.dataIdMap.get(n.dataId).id,Ne=e.dataIdMap.get(s.dataId).id;return qF(ye,Ne,f,h,g,b,C,x,k,_,w,E,A,R,D,V,z,U,K,H,q,Z,ee,oe,J,te,ce),ie}var jF={kernelName:Go,backendName:\"wasm\",setupFunc:O9,kernelFunc:P9};var XF=Ve(Ho);var YF=Ve(Ko);var 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G9(r){let{backend:e,inputs:t,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=e.makeOutput(f,\"float32\"),x=e.dataIdMap.get(n.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),C=new Uint8Array(new Int32Array(f).buffer),w=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),k=e.dataIdMap.get(h.dataId).id;return oO(x,s,a===\"NHWC\"?1:0,b,n.shape.length-1,C,w,f.length,k),h}var nO={kernelName:Yo,backendName:\"wasm\",setupFunc:U9,kernelFunc:G9};var sO;function H9(r){sO=r.wasm.cwrap(Qo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function 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ds=O();ds.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);ds.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);ds.registerFlag(\"WEBGPU_MATMUL_PROGRAM_TYPE\",()=>-1);ds.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);ds.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);ds.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);ds.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);ds.registerFlag(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\",()=>!0);ds.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\",()=>!1);ds.registerFlag(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\",()=>0);ds.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);var kg=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith(\"gen\"))return Number(this.architecture.match(/\\d+/));if(this.architecture.startsWith(\"xe\"))return 12}return 0}isIntel(){return this.vendor===\"intel\"}};var Ng=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,o=!1){let n=$3(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 a=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(a),a}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t,mappedAtCreation:o});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,o){if(this.freeBuffers.size===0)return;let n=$3(t,o);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let s=this.usedBuffers.get(n),a=s.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");s.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,o){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,o)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function $3(r,e){return`${r}_${e}`}var Tg=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,o,n){let s=A3(o),a=e*t*s,i=E3(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e,t,o,n,s){if(this.freeTextures.size===0)return;let a=E3(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=A3(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function E3(r,e,t,o){return`${r}_${e}_${t}_${o}`}function A3(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function R3(r,e){if(Math.max(...r)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let t=r.length,o=r.map(s=>`${e}[${s}]`),n=new Array(t-1);n[t-2]=o[t-1];for(let s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}var Ic=(r,e,t)=>t===\"int32\"?`atomicAdd(${r}, bitcast<i32>(${e}));`:`\n          {\n            var oldValue = 0;\n            loop {\n              let newValueF32 = bitcast<f32>(oldValue) + (${e});\n              let newValue = bitcast<i32>(newValueF32);\n              let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n              if res.exchanged {\n                break;\n              }\n              oldValue = res.old_value;\n            }\n          }`;var M3=(r,e,t,o)=>{let n={dtype:o.dtype,shape:o.shape},s=Yee(t,n,e),a=r.createShaderModule({code:s,label:e.constructor.name});return r.createComputePipeline({compute:{module:a,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"})};function Rt(r){if(r<=1)return\"i32\";if(r===2)return\"vec2<i32>\";if(r===3)return\"vec3<i32>\";if(r===4)return\"vec4<i32>\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Ao(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function Q(...r){let e;switch(r.length){case 0:e=`\n        fn main()\n      `;break;case 1:e=`\n        fn main(${r[0]} : i32)\n      `;break;default:throw Error(\"Unreachable\")}return e}function D3(r,e){let t;return t=`\n     ${Xee(e)}\n      fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n                @builtin(global_invocation_id) GlobalId : vec3<u32>,\n                @builtin(local_invocation_index) LocalIndex: u32,\n                @builtin(workgroup_id) WorkgroupId : vec3<u32>,\n                @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {\n        localId = LocalId;\n        localIndex = LocalIndex;\n        globalId = GlobalId;\n        numWorkgroups = NumWorkgroups;\n        workgroupId = WorkgroupId;\n        ${r?\"main(getGlobalIndex());\":\"main();\"};\n      }\n    `,t}function Xee(r){return`\n  @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function Yee(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(o.push(`\n\n      var<private> localId: vec3<u32>;\n      var<private> localIndex: u32;\n      var<private> globalId: vec3<u32>;\n      var<private> numWorkgroups: vec3<u32>;\n      var<private> workgroupId: vec3<u32>;\n\n      // Only used when the y/z dimension of workgroup size is 1.\n      fn getGlobalIndex() -> i32 {\n        ${B3(t)?\"  return i32(globalId.x);\":`  return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n                workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n                localIndex);\n        `}\n      }\n    `),t.isFromPixels){o.push(`\n        struct Uniform {\n          size            : i32,\n          numChannels     : i32,\n          outShapeStrides : vec2<i32>,\n        };\n\n        @group(0) @binding(0) var<storage, read_write> result: array<${vc(e.dtype,t.isVec4)}>;\n        @group(0) @binding(2) var<uniform> uniforms: Uniform;\n      `);let f=P3(t);return[F3,o.join(`\n`),O3(e.shape),t.getUserCode(),D3(f,t)].join(`\n`)}let s=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=Rt(r[h].shape.length);s+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `});let a=Rt(e.shape.length);s+=`outShape : ${a}, `;let i=e.shape.length-1,p=Rt(i);s+=`\n         outShapeStrides: ${p}, `,t.size&&(s+=\"size : i32, \"),t.uniforms&&(s+=t.uniforms),s+=\"};\",s=nte(s),o.push(s),t.atomic?o.push(`\n      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n    `):o.push(`\n      @group(0) @binding(0) var<storage, read_write> result: array<${vc(e.dtype,t.isVec4)}>;\n    `),t.variableNames.forEach((f,h)=>{o.push(`\n      @group(0) @binding(${1+h}) var<storage, read> ${f}: array<${t.variableTypes?t.variableTypes[h]:vc(r[h].dtype,t.isVec4)}>;\n        `)}),s!==\"\"&&o.push(`\n      @group(0) @binding(${1+t.variableNames.length}) var<uniform> uniforms: Uniforms;\n      `);let u=tte(e.shape,t.dispatchLayout),c=[F3,o.join(`\n`)+Qee,O3(e.shape),u,rte(e.shape.length)];t.atomic||c.push(ote(e.shape,e.dtype,t.isVec4));let l=r.map((f,h)=>ete(f,e.shape,t.variableTypes?t.variableTypes[h]===\"vec4<f32>\":t.isVec4,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=P3(t);return c.push(D3(m,t)),c.join(`\n`)}function L3(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>S.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=B3(r)?\"flatDispatch\":\"\";return n+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+e.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,n}var F3=`\n  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\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  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n  }\n  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let modulo: i32 = a % b;\n    if (sign < 0. && modulo != 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    let floatToUint: vec4<u32> = bitcast<vec4<u32>>(val);\n    return (floatToUint & vec4<u32>(0x7fffffffu)) > vec4<u32>(0x7f800000u);\n  }\n`,Qee=`\n  fn isinf(val: f32) -> bool {\n    return abs(val) == uniforms.INFINITY;\n  }\n`;function O3(r){let e=r.length;if(e<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let t=y.computeStrides(r),o=Rt(e),n=[];for(let a=0;a<e;a++)n.push(`d${a}`);if(t.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 s;return s=\"var index2 = index;\"+t.map((a,i)=>{let p=`let ${n[i]} = index2 / uniforms.outShapeStrides.${Ao(i)}`,u=i===t.length-1?`let ${n[i+1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`:`index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`;return`${p}; ${u};`}).join(\"\"),`\n    fn getCoordsFromIndex(index : i32) -> ${o} {\n      ${s}\n      return ${o}(${n.join(\",\")});\n    }\n  `}function Zee(r,e){let t=r.name,o=r.shape.length,n=Rt(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return e?`\n        fn ${s}() -> vec4<f32> {\n          return vec4<f32>(${t}[0]);\n        }\n      `:`\n      fn ${s}() ->f32 {\n        return f32(${t}[0]);\n      }\n    `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),e?`\n      fn ${s}(${i}) -> vec4<f32> {\n        return vec4<f32>(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n          ${p}) / 4]);\n      }\n      `:`\n    fn ${s}(${i}) -> f32 {\n      return f32(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n        ${p})]);\n    }\n   `}function Jee(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=Rt(p);if(y.arraysEqual(r.shape,e)&&o)return t?`\n      fn ${a}Index(globalIndex : i32) -> vec4<f32> {\n        return vec4<f32>(${n}[globalIndex]);\n      }\n\n      fn ${a}Coords(coords : ${u}) -> vec4<f32> {\n        return vec4<f32>(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n      }\n      `:`\n    fn ${a}Index(globalIndex : i32) -> f32 {\n      return f32(${n}[globalIndex]);\n    }\n\n    fn ${a}Coords(coords : ${u}) -> f32 {\n      return f32(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n    }\n    `;let c=S.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return t?`\n    fn ${a}Index(globalIndex : i32) -> vec4<f32> {\n      return get${s}();\n    }\n\n    fn ${a}Coords(coords : ${u}) -> vec4<f32> {\n      return get${s}();\n    }\n  `:`\n    fn ${a}Index(globalIndex : i32) -> f32{\n      return get${s}();\n    }\n\n    fn ${a}Coords(coords : ${u}) -> f32{\n      return get${s}();\n    }\n  `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Ao(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=Rt(i),x=r.shape.map((b,C)=>`coords.${Ao(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return t?`\n    fn ${a}Index(globalIndex : i32) -> vec4<f32> {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${m}\n      return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n    }\n\n    fn ${a}Coords(coordsIn : ${u}) -> vec4<f32> {\n      var coords = coordsIn;\n      ${m}\n      return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n    }\n  `:`\n  fn ${a}Index(globalIndex : i32) -> f32 {\n    var coords = getCoordsFromIndex(globalIndex);\n    ${m}\n    return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n  }\n\n  fn ${a}Coords(coordsIn : ${u}) -> f32 {\n    var coords = coordsIn;\n    ${m}\n    return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n  }\n`}function ete(r,e,t,o){let n=Zee(r,t);return r.shape.length<=e.length&&(n+=Jee(r,e,t,o)),n}function tte(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${Rt(s)}{\n    let globalIndex = getGlobalIndex();\n    return getCoordsFromIndex(globalIndex);\n  }\n  `;let i=\"\",p=[t,o,n];for(let m=0;m<p.length;m++){let d=p[m];if(d.length!==0)if(d.length===1)i+=`let d${d[0]} = i32(globalId[${m}]);`;else{let f=R3(d,\"uniforms.outShape\");i+=`var index${m} = i32(globalId[${m}]);`;for(let h=0;h<f.length;h++)i+=`let d${d[h]} = index${m} / ${f[h]};`,h===f.length-1?i+=`let d${d[h+1]} = index${m} - d${d[h]} * ${f[h]};`:i+=`index${m} = index${m} - d${d[h]} * ${f[h]};`}}let u=[];for(let m=0;m<a;m++)u.push(`d${m}`);let c=Rt(a),l=`fn getOutputCoords() -> ${c} {\n  ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function rte(r){let e=\"\";switch(r){case 0:case 1:e+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:e+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:e+=`\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:e+=`\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;case 5:e+=`\n        fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u;\n        }\n        `;break;case 6:e+=`\n        fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u * uniforms.outShapeStrides.u +\n              coords.v;\n        }\n        `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function B3(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function vc(r,e){return r===\"float32\"?e?\"vec4<f32>\":\"f32\":r===\"int32\"||r===\"bool\"?e?\"vec4<i32>\":\"i32\":r}function ote(r,e,t){let o=r.length,n=vc(e,t),s;if(t?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${n}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${n}(value);\n    }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${n}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${n}(value);\n    }`,o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=Rt(o);t?s+=`\n      fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:s+=`\n      fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return s}function nte(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function P3(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var hI={};Ge(hI,{GPUBytesPerElement:()=>_g,MatMulProgramType:()=>Ro,assertNotComplex:()=>fI,computeDispatch:()=>Y,computeWorkPerThreadForConv2d:()=>Vl,computeWorkgroupInfoForMatMul:()=>dI,computeWorkgroupSizeForConv2d:()=>Bl,flatDispatchLayout:()=>ae,isWebGPUSupported:()=>zl,tilesFitEvenlyIntoShape:()=>ate});var Ku=r=>{let e=1;for(let t=0;t<r.length;t++)e*=r[t];return e};function ate(r,e){if(r.length!==e.length)throw new Error(`Cannot compute whether rank ${r.length} tiles fit evenly into rank ${e.length} shape - ranks must match.`);return e.every((t,o)=>t%r[o]===0)}function Y(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Ku(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Ku(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Ku(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function dI(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function Bl(r,e,t=!1){if(t)return[8,8,1];let o=Ku(r.x.map(s=>e[s])),n=Ku(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function Vl(r,e,t=!1){if(t)return[4,4,1];let o=Ku(r.x.map(s=>e[s])),n=Ku(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function ae(r){return{x:r.map((e,t)=>t)}}function _g(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function zl(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function fI(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Ro;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Ro||(Ro={}));var ite=O().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),ute=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},Gi=class extends Zr{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!zl())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has(\"timestamp-query-inside-passes\"),this.adapterInfo=new kg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ng(this.device),this.textureManager=new Tg(this.device),this.tensorMap=new Fo(this,sr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),O().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 Gi.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if(\"texture\"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=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,o){if(o===\"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.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o}=t;if(o==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return o}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return this.convertAndCacheOnCPU(e,o);let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=S.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=y.convertBackendValuesAndArrayBuffer(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,o){let n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=_g(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.size<i)throw new Error(`GPUBuffer size(${e.buffer.size}) is smaller than tensor size(${i})!`);if((e.buffer.usage&(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))!==(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))throw new Error(\"GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!\");return e.zeroCopy!==!0&&(n=this.copyBuffer(n,i,n.usage)),a.resourceInfo={size:n.size,usage:n.usage,buffer:n},sr().makeTensorFromDataId(s,t,o,this)}readToGPU(e){let t=this.tensorMap.get(e),{values:o,dtype:n,shape:s,resourceInfo:a}=t;if(n===\"complex64\")throw new Error(\"Does not support reading buffer for complex64 dtype.\");if(a==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let i=a.size,p=this.bufferManager.acquireBuffer(i,a.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,p,0,i),this.submitQueue();let u=this.makeTensorInfo(s,n),c=sr().makeTensorFromTensorInfo(u),l=this.tensorMap.get(u.dataId);return l.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:p},{tensorRef:c,buffer:p,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn(\"This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.\");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if(\"texture\"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=_g(t.dtype)*y.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());if(t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let s=this.bufferManager.acquireUploadBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=s.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o);let i={size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(u=>{u.data.length===0&&(u.data=[1]);let c;switch(u.data.length){case 1:c=4;break;case 2:c=8;break;case 3:c=16;break;case 4:c=16;break;case 5:c=16;break;case 6:c=16;break;default:y.assert(!1,()=>`Unsupported ${u.data.length}D shape`)}(o===5||o===6)&&(c=16),c>s&&(s=c),t=Math.ceil(t/c)*c,o=u.data.length,n.push(t),t+=u.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((u,c)=>{let l=n[c];u.type===\"int32\"?new Int32Array(a,l,u.data.length).set(u.data):u.type===\"uint32\"?new Uint32Array(a,l,u.data.length).set(u.data):new Float32Array(a,l,u.data.length).set(u.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,a,0,t);let p={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(p),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=ute(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),i=t.concat(s).map(x=>x.shape);let h=\"int32\";i.map(x=>{a.push({type:h,data:x})});let g=y.computeStrides(s.shape);if(a.push({type:h,data:g}),e.size){let x=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.isVec4?x/4:x]})}}let p=t.map((h,g)=>{if(h.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(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=L3(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=M3(this.device,e,p,s),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),O().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.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,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=ite){return O().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDataPendingDisposal.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}};Gi.nextDataId=0;zl()&&Ti(\"webgpu\",async()=>{O().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let r={powerPreference:O().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has(\"timestamp-query-inside-passes\")&&(t.requiredFeatures=[\"timestamp-query-inside-passes\"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Gi(n,s)},3);var ge;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.EQUAL=5]=\"EQUAL\",r[r.GREATER=6]=\"GREATER\",r[r.GREATER_EQUAL=7]=\"GREATER_EQUAL\",r[r.INT_DIV=8]=\"INT_DIV\",r[r.LESS=9]=\"LESS\",r[r.LESS_EQUAL=10]=\"LESS_EQUAL\",r[r.LOGICAL_AND=11]=\"LOGICAL_AND\",r[r.LOGICAL_OR=12]=\"LOGICAL_OR\",r[r.MAX=13]=\"MAX\",r[r.MIN=14]=\"MIN\",r[r.MOD=15]=\"MOD\",r[r.MUL=16]=\"MUL\",r[r.NOT_EQUAL=17]=\"NOT_EQUAL\",r[r.POW=18]=\"POW\",r[r.PRELU=19]=\"PRELU\",r[r.SQUARED_DIFFERENCE=20]=\"SQUARED_DIFFERENCE\",r[r.SUB=21]=\"SUB\"})(ge||(ge={}));var V3=`\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `,$g=`\n  resultTemp = select(\n      resultTemp, vec4<f32>(valueForNaN),\n      vec4<bool>(isNaN) | isnanVec4(a) | isnanVec4(b));\n  `,pte=\"return a + b;\",cte=\"return areal * breal - aimag * bimag;\",lte=\"return areal * bimag + aimag * breal;\",mte=\"return a / b;\",dte=\"return f32(a == b);\",fte=\"return vec4<f32>(a == b);\",hte=\"return f32(a > b);\",gte=\"return vec4<f32>(a > b);\",xte=\"return f32(a >= b);\",yte=\"return vec4<f32>(a >= b);\",bte=`\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`,Cte=`\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`,Ste=\"return f32(a < b);\",wte=\"return vec4<f32>(a < b);\",Ite=\"return f32(a <= b);\",vte=\"return vec4<f32>(a <= b);\",kte=\"return f32(a >= 1.0 && b >= 1.0);\",Nte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,Tte=\"return f32(a >= 1.0 || b >= 1.0);\",_te=`return min(vec4<f32>(a >= vec4<f32>(1.0)) +\n  vec4<f32>(b >= vec4<f32>(1.0)), vec4<f32>(1.0));`,$te=`\n  ${V3}\n  if (b == 0.) {\n    return uniforms.NAN;\n  }\n  var resultTemp = a % b;\n  if ((a < 0. && b < 0.) || (a >= 0. && b > 0.)) {\n    return resultTemp;\n  } else {\n    return (resultTemp + b) % b;\n  }\n`,Ete=`\n  let isNaN = !vec4<bool>(b);\n  let valueForNaN = uniforms.NAN;\n  var resultTemp = vec4<f32>(a % b);\n  ${$g}\n\n  if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n    resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n  }\n  if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n    resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n  }\n  if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n    resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n  }\n  if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n    resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n  }\n\n  return resultTemp;\n`,Ate=\"return a * b;\",Rte=`\n  if (isnan(a) || isnan(b)) {\n    return 1.0;\n  }\n  return f32(a != b);\n`,Dte=`\n  var resultTemp = vec4<f32>(a != b);\n  let valueForNaN = 1.0;\n  ${$g}\n\n  return resultTemp;\n`,Fte=`\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`,Ote=`\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  let valueForNaN = uniforms.NAN;\n  ${$g}\n  return resultTemp;\n`,Pte=\"if (a < 0.0) { return b * a; }  return a;\",Mte=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`,Lte=\"return (a - b) * (a - b);\",Bte=\"return a - b;\";function gI(r,e,t=\"uniforms.NAN\"){let o=e?$g:V3;return e?`\n    let valueForNaN = ${t};\n    var resultTemp = vec4<f32>(${r}(a, b));\n    `+o+`\n    return resultTemp;\n  `:o+`\n    return ${r}(a, b);\n  `}function kc(r,e){switch(r){case ge.ADD:return pte;case ge.ATAN2:return gI(\"atan2\",e);case ge.COMPLEX_MULTIPLY_IMAG:return lte;case ge.COMPLEX_MULTIPLY_REAL:return cte;case ge.DIV:return mte;case ge.EQUAL:return e?fte:dte;case ge.GREATER:return e?gte:hte;case ge.GREATER_EQUAL:return e?yte:xte;case ge.INT_DIV:return e?Cte:bte;case ge.LESS:return e?wte:Ste;case ge.LESS_EQUAL:return e?vte:Ite;case ge.LOGICAL_AND:return e?Nte:kte;case ge.LOGICAL_OR:return e?_te:Tte;case ge.MAX:return gI(\"max\",e);case ge.MIN:return gI(\"min\",e);case ge.MOD:return e?Ete:$te;case ge.MUL:return Ate;case ge.NOT_EQUAL:return e?Dte:Rte;case ge.POW:return e?Ote:Fte;case ge.PRELU:return e?Mte:Pte;case ge.SQUARED_DIFFERENCE:return Lte;case ge.SUB:return Bte;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var X;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(X||(X={}));var Vte=\"return abs(a);\",zte=`\n  if (abs(a) > 1.) {\n    return uniforms.NAN;\n  }\n  return acos(a);\n`,Wte=`\n  if (a < 1.) {\n    return uniforms.NAN;\n  }\n  return acosh(a);\n`,Ute=`\n  if (abs(a) > 1.) {\n    return uniforms.NAN;\n  }\n  return asin(a);\n`,Gte=\"return asinh(a);\",Hte=`\n  if (isnan(a)) {\n    return uniforms.NAN;\n  }\n  return atan(a);\n`,Kte=`\n  if (abs(a) > 1.) {\n    return uniforms.NAN;\n  }\n  if (a == 1.) {\n    return uniforms.INFINITY;\n  }\n  if (a == -1.) {\n    return -uniforms.INFINITY;\n  }\n  return atanh(a);\n`,qte=\"return ceil(a);\",jte=\"return cos(a);\",Xte=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,Yte=\"return exp(a) - 1.0;\",Qte=\"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`,Jte=`\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  let p = ${S.ERF_P};\n  let a1 = ${S.ERF_A1};\n  let a2 = ${S.ERF_A2};\n  let a3 = ${S.ERF_A3};\n  let a4 = ${S.ERF_A4};\n  let a5 = ${S.ERF_A5};\n\n  let sign = sign(a);\n  let absA = abs(a);\n  let t = 1.0 / (1.0 + p * absA);\n  return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,ere=\"return exp(a);\",tre=\"return floor(a);\",rre=\"return f32(!isnan(a) && !isinf(a));\",ore=\"return f32(isinf(a));\",nre=\"return f32(isnan(a));\",sre=\"return a;\",are=`if (a < 0.0) { return uniforms.NAN; }\n  return log(a);`,ire=`\n  if (isnan(a)) { return a; }\n  return log(1.0 + a);\n`,ure=\"return f32(!(a >= 1.0));\",pre=\"return -a;\",cre=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",lre=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`,mre=\"return 1.0 / a;\",dre=\"return select(a, 0.0, a < 0.0);\",fre=\"return clamp(a, 0.0, 6.0);\",hre=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",gre=`\n  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));\n`,xre=\"return round(a);\",yre=\"return inverseSqrt(a);\",bre=`\n  if (a >= 0.0) {\n    return ${S.SELU_SCALE} * a;\n  } else {\n    return ${S.SELU_SCALEALPHA} * (exp(a) - 1.0);\n  }\n`,Cre=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Sre=\"return sign(a);\",wre=\"return sin(a);\",Ire=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,vre=`\n  let epsilon = 1.1920928955078125e-7;\n  let threshold = log(epsilon) + 2.0;\n\n  let too_large = a > -threshold;\n  let too_small = a < threshold;\n  let exp_a = exp(a);\n\n  if (too_large) {\n    return a;\n  } else if (too_small) {\n    return exp_a;\n  } else {\n    return log(exp_a + 1.0);\n  }\n`,kre=\"return sqrt(a);\",Nre=\"return a * a;\",Tre=`\n  if (isnan(a)) {\n    return a;\n  }\n\n  return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,_re=\"return tan(a);\",$re=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Ere=\"return f32(i32((a)));\";function ja(r,e){switch(r){case X.ABS:return Vte;case X.ACOS:return zte;case X.ACOSH:return Wte;case X.ASIN:return Ute;case X.ASINH:return Gte;case X.ATAN:return Hte;case X.ATANH:return Kte;case X.COS:return jte;case X.COSH:return Xte;case X.CEIL:return qte;case X.ELU:return e?Zte:Qte;case X.ERF:return Jte;case X.EXP:return ere;case X.EXPM1:return Yte;case X.FLOOR:return tre;case X.IS_FINITE:return rre;case X.IS_INF:return ore;case X.IS_NAN:return nre;case X.LINEAR:return sre;case X.LOG:return are;case X.LOG1P:return ire;case X.LOGICAL_NOT:return ure;case X.NEG:return pre;case X.LEAKYRELU:return e?lre:cre;case X.RECIPROCAL:return mre;case X.RELU:return e?gre:dre;case X.RELU6:return e?hre:fre;case X.ROUND:return xre;case X.RSQRT:return yre;case X.SELU:return bre;case X.SIGMOID:return Cre;case X.SIGN:return Sre;case X.SIN:return wre;case X.SINH:return Ire;case X.SOFTPLUS:return vre;case X.SQRT:return kre;case X.SQUARE:return Nre;case X.STEP:return Tre;case X.TAN:return _re;case X.TANH:return $re;case X.TO_INT:return Ere;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var kt=r=>{switch(r){case 1:return\"f32\";case 2:return\"vec2<f32>\";case 3:return\"vec3<f32>\";case 4:return\"vec4<f32>\";default:throw new Error(`${r}-component is not supported.`)}};function pr(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=ja(X.LINEAR);else if(r===\"relu\")n=ja(X.RELU,t);else if(r===\"elu\")n=ja(X.ELU,t);else if(r===\"relu6\")n=ja(X.RELU6,t);else if(r===\"prelu\")n=kc(ge.PRELU,t);else if(r===\"sigmoid\")n=ja(X.SIGMOID,t);else if(r===\"leakyrelu\")n=ja(X.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=kt(t?4:1),i=\"\";return e?i=`\n      fn activation(a : ${a}, coords : vec${o}<i32>) -> ${a} {\n        let b = getPreluActivationWeightsByOutputCoords(coords);\n        ${n}\n      }`:i=`\n      fn activation(a : ${a}, coords : vec${o}<i32>) -> ${a} {\n        ${n}\n      }`,i}function Gr(r,e){return`\n      ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n      ${e?\"value = activation(value, coords);\":\"\"}\n      `}function xI(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n      ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n    `,i=e?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n  fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${kt(s)} {\n    var value = ${kt(s)}(0.0);\n    let col = colIn * ${s};\n    ${t&&n?a:`\n    ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n    {\n      ${a}\n    }\n    `}\n    return value;\n  }\n\n  fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${kt(s)} {\n    let col = colIn * ${s};\n    var value = ${kt(s)}(0.0);\n    ${i}\n    return value;\n  }\n  `}function Wl(r,e,t,o,n=!1,s=!1,a=!1,i=1){return`\n  ${xI(t,o,n,s,a,i)}\n  fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(i)}) {\n    let col = colIn * ${i};\n    ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n    {\n      var value = valueIn;\n      let coords = vec3<i32>(batch, row, col);\n      ${Gr(r,e)}\n      setOutputAtCoords(coords[0], coords[1], coords[2], value);\n    }\n  }\n  `}var Are=(r,e)=>r?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n          kStart + inputRow,\n          globalRowStart / ${e} + inputCol);\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n          globalRow + innerRow,\n          kStart / ${e} + inputCol);\n        `,Rre=(r,e,t)=>r?`\n        let ACached0 = mm_Asub[k * ${e}][localRow];\n        let ACached1 = mm_Asub[k * ${e} + 1][localRow];\n        let ACached2 = mm_Asub[k * ${e} + 2][localRow];\n        ${e===3?\"\":`let ACached3 = mm_Asub[k * ${e} + 3][localRow];`}\n        for (var i = 0; i < ${t}; i++) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${e===3?\"\":\"acc[i] = BCached3 * ACached3[i] + acc[i];\"}\n        }`:`\n        for (var i = 0; i < ${t}; i++) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${e===3?\"\":\"acc[i] = BCached3 * ACached.w + acc[i];\"}\n        }`;function qu(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=e[1]*r[1],u=e[0]*r[0],c=t?p:o,l=t?o:p,m=c/e[0],d=o/e[1],f=r[1];return y.assert((t&&m===4&&r[1]===4||!t&&(m===3||m===4))&&c%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${m} and workPerThread[1] ${r[1]} must be 4.\n          Otherwise, innerElementSize ${m} must be 3 or 4.\n      tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n  var<workgroup> mm_Asub : array<array<vec${m}<f32>, ${c/m}>, ${l}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${u/r[0]}>, ${o}>;\n\n  ${Q()} {\n    let localRow = i32(localId.y);\n    let tileRow = ${a?\"0\":`localRow * ${f}`};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${a?\"0\":`i32(globalId.y) * ${f}`};\n    let globalCol = i32(globalId.x);\n    let batch = ${n?\"0\":\"i32(globalId.z)\"};\n    let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n    let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n    let globalRowStart = i32(workgroupId.y) * ${p};\n\n    let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n    var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n    var acc: array<vec4<f32>, ${f}>;\n\n    // Loop over shared dimension.\n    let tileRowB = localRow * ${d};\n    for (var t = 0; t < numTiles; t++) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            ${Are(t,m)}\n        }\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n        }\n        kStart = kStart + ${o};\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${o/m}; k++) {\n            let BCached0 = mm_Bsub[k * ${m}][tileCol];\n            let BCached1 = mm_Bsub[k * ${m} + 1][tileCol];\n            let BCached2 = mm_Bsub[k * ${m} + 2][tileCol];\n            ${m===3?\"\":`let BCached3 = mm_Bsub[k * ${m} + 3][tileCol];`}\n\n            ${Rre(t,m,f)}\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n        mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n    }\n  }`}var z3=r=>r?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n          kStart + inputRow,\n          globalRowStart + inputCol);\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n          globalRowStart + inputRow,\n          kStart + inputCol);\n        `,Dre=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function ju(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?`\n      let localRow = i32(localId.y);\n      let localCol = i32(localId.x);\n      let globalRowStart = i32(workgroupId.y) * ${p};\n      let globalColStart = i32(workgroupId.x) * ${u};\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t++) {\n        // Load one tile of A into local memory.\n        for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {\n          for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {\n            ${z3(t)}\n          }\n        }\n        // Load one tile of B into local memory.\n        for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n              for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n            mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n              kStart + inputRow,\n              globalColStart + inputCol);\n          }\n        }\n        kStart = kStart + ${o};\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        var BCached : array<f32, ${g}>;\n        for (var k = 0; k < ${o}; k++) {\n          for (var inner = 0; inner < ${g}; inner++) {\n            BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n          }\n          for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n            let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n            for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n                  ACached * BCached[innerCol];\n            }\n          }\n        }\n        workgroupBarrier();\n      }\n      for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n        let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n        for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n          let gCol = globalColStart + localCol + innerCol * ${e[0]};\n          mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n        }\n      }\n      `:`\n  let tileRow = i32(localId.y) * ${h};\n  let tileCol = i32(localId.x) * ${g};\n\n  let globalRow = i32(globalId.y) * ${h};\n  let globalCol = i32(globalId.x) * ${g};\n  let globalRowStart = i32(workgroupId.y) * ${p};\n\n  let tileRowA = i32(localId.y) * ${m};\n  let tileColA = i32(localId.x) * ${d};\n  let tileRowB = i32(localId.y) * ${f};\n  // Loop over shared dimension.\n  for (var t = 0; t < numTiles; t++) {\n    // Load one tile of A into local memory.\n    for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n      for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n        let inputRow = tileRowA + innerRow;\n        let inputCol = tileColA + innerCol;\n        ${z3(t)}\n      }\n    }\n\n    // Load one tile of B into local memory.\n    for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n      for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n        let inputRow = tileRowB + innerRow;\n        let inputCol = tileCol + innerCol;\n        mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n          kStart + inputRow,\n          globalCol + innerCol);\n      }\n    }\n    kStart = kStart + ${o};\n    workgroupBarrier();\n\n    // Compute acc values for a single thread.\n    var BCached : array<f32, ${g}>;\n    for (var k = 0; k < ${o}; k++) {\n      for (var inner = 0; inner < ${g}; inner++) {\n        BCached[inner] = mm_Bsub[k][tileCol + inner];\n      }\n\n      for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n        ${Dre(t)}\n        for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n          acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n        }\n      }\n    }\n\n    workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n    for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n      mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n          acc[innerRow][innerCol]);\n    }\n  }\n  `;return`\n    var<workgroup> mm_Asub : array<array<f32, ${c}>, ${l}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${u}>, ${o}>;\n\n    ${Q()} {\n      let batch = ${n?\"0\":\"i32(globalId.z)\"};\n      let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n      let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n      let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n      var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n      var acc : array<array<f32, ${g}>, ${h}>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n        for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n      ${x}\n    }\n  `}var Fre=r=>r?`\n      mm_readA(batchA, colA, globalRow),\n      mm_readA(batchA, colA + 1, globalRow),\n      mm_readA(batchA, colA + 2, globalRow),\n      mm_readA(batchA, colA + 3, globalRow)\n  `:`\n      mm_readA(batchA, globalRow, colA),\n      mm_readA(batchA, globalRow, colA + 1),\n      mm_readA(batchA, globalRow, colA + 2),\n      mm_readA(batchA, globalRow, colA + 3)\n  `;function Ore(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return`\n    var<workgroup> mm_Asub : array<vec4<f32>, ${r[0]}>;\n\n    ${Q()} {\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) / ${t} + 1;\n      let batch = i32(globalId.z);\n      let batchA = batch % uniforms.aShape[0];\n      let batchB = batch % uniforms.bShape[0];\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++) {\n        // Load one tile of A into local memory.\n        let colA = t * ${t} + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(${Fre(e)});\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${t/4}; k++) {\n          let rowB = t * ${t} + k * 4;\n          let BCached = vec4<f32>(mm_readB(batchB, rowB, globalCol),\n                              mm_readB(batchB, rowB + 1, globalCol),\n                              mm_readB(batchB, rowB + 2, globalCol),\n                              mm_readB(batchB, rowB + 3, globalCol));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      mm_write(batch, globalRow, globalCol, acc);\n    }\n  `}var Eg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=dI(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n      ${pr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n      ${Wl(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n      ${this.isVec4?qu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA,!0):this.isVectorA?Ore(this.workgroupSize,this.transposeA):ju(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n    `}};function Pre(r){return`\n    var<workgroup> sumValues : array<f32, ${r}>;\n    ${Q()} {\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let batchA = batch % uniforms.aShape[0];\n      let batchB = batch % uniforms.bShape[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 + ${r}) {\n        let dataA = mm_readA(batchA, row, k);\n        let dataB = mm_readB(batchB, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = ${r/2}u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var Ag=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return`\n      ${pr(this.activation,this.hasPreluActivationWeights)}\n      ${Wl(this.addBias,this.activation,this.transposeA,this.transposeB)}\n      ${Pre(this.workgroupSize[0])}\n    `}};function Mre(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n  var<workgroup> mm_Asub : array<array<f32, ${o}>, ${e}>;\n  var<workgroup> mm_Bsub : array<array<f32, ${t}>, ${o}>;\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  // Read data from global memory to registers firstly, then store them into\n  // shared memory, so it is instruction-Level parallelism for arithmetic\n  // operations and others handle IO operations between barrier api, makes ALU\n  // and load/store units work simultaneously, could improves the performance.\n  ${Q()} {\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    let batch = i32(globalId.z);\n    let batchA = batch % uniforms.aShape[0];\n    let batchB = batch % uniforms.bShape[0];\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = 0;\n    var regA = mm_readA(batchA, globalRow, globalColA);\n    var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n    var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n    globalColA = globalColA + ${o};\n    globalRowB = globalRowB + ${o};\n\n    for (var t = 0; t < numTiles; t = t + 1) {\n      mm_Asub[tileRow][tileCol] = regA;\n      mm_Bsub[2 * tileRow][tileCol] = regB0;\n      mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n      workgroupBarrier();\n\n      regA = mm_readA(batchA, globalRow, globalColA);\n      regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n      regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n      globalColA = globalColA + ${o};\n      globalRowB = globalRowB + ${o};\n\n      for (var k = 0; k < ${o}; k = k + 1) {\n        acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n      }\n      workgroupBarrier();\n    }\n\n    mm_write(batch, globalRow, globalCol, acc);\n  }\n  `}var Rg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n      ${pr(this.activation,this.hasPreluActivationWeights)}\n      ${Wl(this.addBias,this.activation,this.transposeA,this.transposeB)}\n      ${Mre(this.workgroupSize)}\n    `}};var Dg=class{constructor(e,t,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=Y(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=this.isVec4?4:1;return`\n      ${xI(!1,this.transposeB,!1,!1,!1,e)}\n      fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(e)}) {\n        let col = colIn * ${e};\n        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n          let coords = vec3<i32>(batch, row, col);\n          let flatIndex = getOutputIndexFromCoords(coords);\n          // The problem is that we should initialize output to zero before using.\n          // Otherwise, the original value will be added to the result.\n          for (var i = 0; i < ${e}; i = i + 1) {\n            ${Ic(\"&result[flatIndex + i]\",`${e>1?\"value[i]\":\"value\"}`,\"float32\")}\n          }\n        }\n      }\n      ${this.isVec4?qu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):ju(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n    `}},Fg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n    ${pr(this.activation,this.hasPreluActivationWeights)}\n    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        var value = getXByOutputIndex(index);\n        ${Gr(this.addBias,this.activation)}\n        setOutputAtIndex(index, value);\n      }\n    }\n    `}};var Og=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function fr(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Og(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var W3={kernelName:ws,backendName:\"webgpu\",kernelFunc:fr};function me(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var U3={kernelName:_s,backendName:\"webgpu\",kernelFunc:me};function Xu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=yr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=me({inputs:{x:r},backend:n,attrs:{shape:k}}),A=me({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[E,A],D=Math.max(x,b),P=[E,A],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,V,z=[D,d,f],U=O().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let H=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),q=H>0?H:n.thresholdToIncreaseWorkgroups,Z=D*Math.ceil(d/32)*Math.ceil(f/32);Z<=q||d<=8&&Z<=q*2?D*d*f<=128?U=Ro.MatMulReduceProgram:D===1&&m>=2e3?U=Ro.MatMulSplitKProgram:U=Ro.MatMulSmallOutputSizeProgram:U=Ro.MatMulPackedProgram}switch(U){case Ro.MatMulReduceProgram:L=new Ag(z,t,o,s,p,a);break;case Ro.MatMulSplitKProgram:{if(V=fr({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Dg(z,m,t,o),s||p){V=n.runWebGPUProgram(L,P,r.dtype,M,V);let q=new Fg(V.shape,s,p,a),Z=null,ee=[V];s&&ee.push(s),a&&ee.push(a),p===\"leakyrelu\"&&(Z=[{type:\"float32\",data:[i]}],q.uniforms+=\" alpha : f32,\");let oe=n.runWebGPUProgram(q,ee,V.dtype,Z);R.push(V);let J=me({inputs:{x:oe},backend:n,attrs:{shape:w}});R.push(oe);for(let te of R)n.disposeData(te.dataId);return J}break}case Ro.MatMulSmallOutputSizeProgram:L=new Rg(k,_,z,t,o,s,p,a);break;case Ro.MatMulPackedProgram:let H=n.adapterInfo.isIntel();L=new Eg(k,z,t,o,s,p,a,H);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&P.push(s),a&&P.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),V=n.runWebGPUProgram(L,P,r.dtype,M,V);let K=me({inputs:{x:V},backend:n,attrs:{shape:w}});R.push(V);for(let H of R)n.disposeData(H.dataId);return K}function Lre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Xu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var G3={kernelName:ho,backendName:\"webgpu\",kernelFunc:Lre};var Ul=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${kc(this.op,!1)}\n      }\n\n      ${Q(\"index\")} {\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}};var Yu=class{constructor(e,t,o){this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ae(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type=\"shared\",this.workgroupSize=[256,1,1],this.workPerThread=1):(y.arraysEqual(t,o)&&y.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type=\"vec4\",this.workPerThread=4):(this.isVec4=!1,this.type=\"plain\",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?\"vec4<f32>\":\"f32\",o=`\n    fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n      let isNaN = false;\n      {\n        ${kc(this.op,this.isVec4)}\n      }\n    };\n    `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n          let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n          let b = getBByOutputIndex(index);`;e=`\n        ${o}\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Q(\"index\")} {\n          // Fill in the shared memory buffer.\n          let localIndex = i32(localId.x);\n          if(localIndex < ${this.lastDimensionSize}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          if(index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            ${s}\n            setOutputAtIndex(index, binaryOperation(a, b));\n          }\n        }\n        `}else e=`\n       ${o}\n       ${Q(\"index\")} {\n         if (index < uniforms.size) {\n           let a = getAByOutputIndex(index);\n           let b = getBByOutputIndex(index);\n           setOutputAtIndex(index, binaryOperation(a, b));\n         }\n       }\n       `;return e}};function Dt(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var H3={kernelName:mo,backendName:\"webgpu\",kernelFunc:Dt};function po(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var K3={kernelName:ri,backendName:\"webgpu\",kernelFunc:po};var Hr=class{constructor(e,t,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${ja(this.op,!1)}\n      }\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function xe({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Hr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function Ze({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==ge.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},w={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Yu(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,w],dt(x.dtype,b.dtype))});else{let g=new Ul(ge.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new Ul(ge.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{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},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=po({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?S.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Yu(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:q3,castImpl:j3,ceilImpl:X3,concatImpl:Y3,equalImpl:Q3,expImpl:Z3,expm1Impl:J3,floorImpl:eM,gatherNdImpl:tM,gatherV2Impl:rM,greaterEqualImpl:oM,greaterImpl:nM,lessEqualImpl:sM,lessImpl:aM,logImpl:iM,maxImpl:uM,maximumImpl:pM,minimumImpl:cM,multiplyImpl:lM,negImpl:mM,notEqualImpl:dM,prodImpl:fM,rangeImpl:hM,rsqrtImpl:gM,scatterImpl:xM,simpleAbsImpl:yM,sliceImpl:bM,stridedSliceImpl:CM,stringNGramsImpl:SM,subImpl:wM,tileImpl:IM,topKImpl:vM,transposeImpl:kM,uniqueImpl:iTt}=Zp;var Bre=xe({opType:X.ABS,cpuKernelImpl:yM}),NM={kernelName:ys,backendName:\"webgpu\",kernelFunc:Bre};var Vre=xe({opType:X.ACOS}),TM={kernelName:aa,backendName:\"webgpu\",kernelFunc:Vre};var zre=xe({opType:X.ACOSH}),_M={kernelName:ia,backendName:\"webgpu\",kernelFunc:zre};var Wre=Ze({opType:ge.ADD,cpuKernelImpl:q3,supportsComplex:!0}),$M={kernelName:eo,backendName:\"webgpu\",kernelFunc:Wre};var Pg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n      ${Q(\"index\")} {\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 Ure(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Pg(s);return t.runWebGPUProgram(a,o,n)}var EM={kernelName:Po,backendName:\"webgpu\",kernelFunc:Ure};var Mg=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n<o.length;n++)o[n]=e[t[n]];this.outputShape=o,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){y.assert(this.workgroupSize[0]===this.workgroupSize[1],()=>`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`\n      var<workgroup> tile : array<array<f32, ${this.workgroupSize[0]+1}>, ${this.workgroupSize[0]}>;\n      ${Q()} {\n        var x = i32(workgroupId.x) * ${e} + i32(localId.x);\n        var y = i32(workgroupId.y) * ${e} + 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] = f32(A[y * width + x]);\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * ${e} + i32(localId.x);\n        y = i32(workgroupId.x) * ${e} + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}};var Lg=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n<o.length;n++)o[n]=e[t[n]];this.outputShape=o,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Rt(this.outputShape.length),t=Gre(this.newDim);return`\n      ${Q(\"index\")} {\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function Gre(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o<r.length;o++)t[r[o]]=`resRC.${Ao(o)}`;return t.join()}function Nr(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,p=new Array(i);for(let c=0;c<p.length;c++)p[c]=n.shape[s[c]];if(t.shouldExecuteOnCPU([n])){let l=a.tensorMap.get(n.dataId).values,m=kM(l,n.shape,n.dtype,s,p);return t.makeTensorInfo(p,n.dtype,m)}if(n.shape.length===2&&y.arraysEqual(s,[1,0])){let c=new Mg(n.shape,s);return a.runWebGPUProgram(c,[n],n.dtype)}let u=new Lg(n.shape,s);return a.runWebGPUProgram(u,[n],n.dtype)}var AM={kernelName:ro,backendName:\"webgpu\",kernelFunc:Nr};var Bg=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[o]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=o.length===0?[1]:o,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\",o=this.workgroupSize[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[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");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, ${o}>;\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       ${Q(\"index\")} {\n         let outputIndex = index / ${o};\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + ${o}) {\n           let candidate = f32(x[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), ${o}u);\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          ${n}\n        }\n       }\n     `}};function Kr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=S.getAxesPermutation(p,s),c=r;u!=null&&(c=Nr({inputs:{x:r},attrs:{perm:u},backend:n}),p=S.getInnerMostAxes(p.length,s),a.push(c)),S.assertAxesAreInnerMostDims(o,p,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=S.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=uM(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=fM(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=o===\"mean\"?\"float32\":Ta(r.dtype),w=[{type:\"int32\",data:[h]}],k=new Bg(b,o),_=n.runWebGPUProgram(k,[c],C,w);a.push(_),f=me({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function Hre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,\"all\",t)}var RM={kernelName:Mo,backendName:\"webgpu\",kernelFunc:Hre};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,\"any\",t)}var DM={kernelName:Lo,backendName:\"webgpu\",kernelFunc:Kre};var Nc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=S.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ae(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type=\"plain\",this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=Y(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Ao(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s<this.outputShape.length;s++)n+=`outputCoords.${Ao(s)},`;return n};return this.type===\"shared\"?`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${`\n      var<workgroup> xBestIndices : array<i32, ${e}>;\n      var<workgroup> xBestValues : array<f32, ${e}>;\n    `}\n\n      ${Q(\"index\")} {\n        let outputIndex = index / ${e};\n        let reduceLength = ${t()};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n            k = k + ${e}) {\n          let candidate = getX(${o()} 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(reduceLength), ${e}u);\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    `:`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let outputCoords = getCoordsFromIndex(index);\n          var bestIndex = 0;\n          var bestValue = getX(${o()} 0);\n          let reduceLength = ${t()};\n          for (var i = 1; i < reduceLength; i++) {\n            let candidate = getX(${o()} i);\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              bestIndex = i;\n            }\n          }\n          setOutputAtIndexI32(index, bestIndex);\n        }\n      }\n      `}};function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new Nc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var FM={kernelName:Bo,backendName:\"webgpu\",kernelFunc:qre};function jre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new Nc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var OM={kernelName:ei,backendName:\"webgpu\",kernelFunc:jre};var Xre=xe({opType:X.ASIN}),PM={kernelName:ua,backendName:\"webgpu\",kernelFunc:Xre};var Yre=xe({opType:X.ASINH}),MM={kernelName:pa,backendName:\"webgpu\",kernelFunc:Yre};var Qre=xe({opType:X.ATAN}),LM={kernelName:ca,backendName:\"webgpu\",kernelFunc:Qre};var Zre=Ze({opType:ge.ATAN2}),BM={kernelName:ma,backendName:\"webgpu\",kernelFunc:Zre};var Jre=xe({opType:X.ATANH}),VM={kernelName:la,backendName:\"webgpu\",kernelFunc:Jre};var Gl=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=ae(this.outputShape),this.dispatch=Y(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 / max(count, 1.0)\"),`\n      ${Q(\"index\")} {\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    `}};var Vg=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Q(\"index\")} {\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 Hl(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return Kr(n,s,a,\"max\",t)}var zM={kernelName:yn,backendName:\"webgpu\",kernelFunc:Hl};function yI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,\"mean\",t)}var WM={kernelName:Sn,backendName:\"webgpu\",kernelFunc:yI};function zg(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Dt({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=me({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=yI({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=Hl({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=me({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new Vg(e):(t===\"avg\"?n=new Gl(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Gl(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function eoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return zg(n,c,\"avg\",t)}var UM={kernelName:Vo,backendName:\"webgpu\",kernelFunc:eoe};var Wg=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`stride : vec2<i32>, pads : vec2<i32>, dilation : vec2<i32>, filterDims : vec2<i32>,\n       outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avg_pool2d_backprop\"}getUserCode(){return`\n      ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d = coords[3];\n\n        let dyRCCorner = vec2<i32>(coords.yz) - uniforms.pads;\n        let dyRCorner = dyRCCorner.x;\n        let 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        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilation[0]) {\n          let dyR = f32(dyRCorner + wR) / f32(uniforms.stride[0]);\n\n          if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n            continue;\n          }\n          let idyR = i32(dyR);\n\n          for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilation[1]) {\n            let dyC = f32(dyCCorner + wC) / f32(uniforms.stride[1]);\n\n            if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n              continue;\n            }\n            let idyC = i32(dyC);\n\n            let dyValue = getDy(batch, idyR, idyC, d);\n\n            dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n    `}};function toe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;fI([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new Wg(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var GM={kernelName:mp,backendName:\"webgpu\",kernelFunc:toe};function roe(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Xu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var HM={kernelName:zo,backendName:\"webgpu\",kernelFunc:roe};var Ug=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Rt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Rt(this.rank),t=ooe(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${bI[a]} = uniforms.start.${Ao(a)} + coords.${bI[a]};`),`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${o.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},bI=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function ooe(r){if(r===1)return\"sourceLoc\";if(r<=6)return bI.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function fs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=bM(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Ug(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var KM={kernelName:Es,backendName:\"webgpu\",kernelFunc:fs};var noe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=me({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=me({inputs:{x:h},backend:t,attrs:{shape:c}}),x=fs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},qM={kernelName:bs,backendName:\"webgpu\",kernelFunc:noe};var soe=`\n  fn bincount_write(index: i32, value: f32) {\n    ${Ic(\"&result[index]\",\"value\",\"float32\")}\n  }\n`,aoe=`\n  fn bincount_write(index: i32, value: f32) {\n    atomicStore(&result[index], bitcast<i32>(value));\n  }\n`,Tc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n    ${this.binaryOutput?aoe:soe}\n  ${Q(\"index\")} {\n    ${this.rank===1?`if (index < uniforms.xShape) {\n      let indexVal = i32(getX(index));\n      if (indexVal < uniforms.binCountSize) {\n        let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n        bincount_write(indexVal, value);\n      }\n    }`:`let coord = getCoordsFromIndex(index);\n    if (coordsInBounds2D(coord, uniforms.xShape)) {\n      let indexVal = i32(getX(coord[0], coord[1]));\n      if (indexVal < uniforms.binCountSize) {\n        let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n        bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n      }\n    }`}\n  }\n  `}};function ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=fr({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Tc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var jM={kernelName:ti,backendName:\"webgpu\",kernelFunc:ioe};var CI=Ze({opType:ge.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:dM}),XM={kernelName:Nn,backendName:\"webgpu\",kernelFunc:CI};function Xa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var YM={kernelName:di,backendName:\"webgpu\",kernelFunc:Xa};function QM(r,e){let t=new Hr(r.shape,X.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function SI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:t});let a=Br(n.shape),i=SI({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=po({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=Xa({inputs:{input:n},backend:t}),i=SI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=j3(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return QM(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=CI({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var ZM={kernelName:co,backendName:\"webgpu\",kernelFunc:SI};var uoe=xe({opType:X.CEIL,cpuKernelImpl:X3}),JM={kernelName:Wo,backendName:\"webgpu\",kernelFunc:uoe};var Gg=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue = clamp(\n              value, vec4<f32>(uniforms.minVal), vec4<f32>(uniforms.maxVal));\n          clampedValue = select(clampedValue, value, isnanVec4(value));\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}};var Hg=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function poe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new Gg(n.shape):i=new Hg(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var eL={kernelName:lo,backendName:\"webgpu\",kernelFunc:poe};var Kg=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(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${t} : i32,`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;s<this.offsetLength;s++)e.push(`else if (yC < uniforms.offset${[s]}){ setOutputAtCoords(coords.x, coords.y, getT${s}(yR, yC - uniforms.offset${s-1})); }`);let o=this.offsetLength,n=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${o}(yR, yC - uniforms.offset${n})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Q(\"index\")} {\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 Qu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var tL={kernelName:ci,backendName:\"webgpu\",kernelFunc:Qu};function _c(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let f=r.map(C=>Xa({inputs:{input:C},backend:t})),h=r.map(C=>Qu({inputs:{input:C},backend:t})),g=_c(f,e,t),x=_c(h,e,t),b=po({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(e))];return me({inputs:{x:k},backend:t,attrs:{shape:E}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=S.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=Y3(h,g,o,x),C=S.computeOutShape(r.map(k=>k.shape),e),w=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),w}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;g<r.length;g+=s){let x=r.slice(g,g+s);f.push(_c(x,e,t))}let h=_c(f,e,t);for(let g of f)t.disposeData(g.dataId);return h}let{tensors2D:a,outShape:i}=coe(r,e,t),p=a.map(f=>f.shape),u=new Kg(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;f<l.length;f++)l[f]=l[f-1]+p[f][1],c.push({type:\"int32\",data:[l[f]]})}let m=t.runWebGPUProgram(u,a,a[0].dtype,c);a.forEach(f=>t.disposeData(f.dataId));let d=me({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function coe(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>me({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function wI(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):_c(p,s,t)}var rL={kernelName:Cs,backendName:\"webgpu\",kernelFunc:wI};function loe(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=R=>{switch(R){case 1:return\"resData = x[xIndex];\";case 3:return\"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},l=R=>{switch(R){case 1:return\"return W[row * uniforms.wShape[3] + colIn];\";case 4:return\"return W[row * uniforms.wShape[3] / 4 + colIn];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=r?`\n      let coord = vec4<i32>(batch, xRow, xCol, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, xRow, xCol);\n      `,d=r?`\n      let coords = vec4<i32>(\n        batch,\n        row / outWidth,\n        row % outWidth,\n        col);\n      `:`\n      let coords = vec4<i32>(\n        batch,\n        row,\n        col / outWidth,\n        col % outWidth);\n      `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n      let inChannels = uniforms.wShape[2];\n      let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n      let outRow = ${g} / outWidth;\n      let outCol = ${g} % outWidth;\n\n      let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n      let WCol = ${x} / inChannels % uniforms.filterDims[1];\n      let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n      let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n      let xCh = ${x} % inChannels;\n      var resData = ${kt(i)}(0.0);\n      // The bounds checking is always needed since we use it to pad zero for\n      // the 'same' padding type.\n      if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n        ${m}\n        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n        ${c(i)}\n      }\n      return resData;`,C=r?e&&o?`\n      let col = colIn * ${i};\n      ${b}`:`\n      let col = colIn * ${i};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${b}\n      }\n      return ${kt(i)}(0.0);`:o&&t?`\n      let col = colIn * ${i};\n      ${b}`:`\n      let col = colIn * ${i};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${b}\n      }\n      return ${kt(i)}(0.0);`,w=`${l(p)}`,k=kt(u),_=r?kt(i):kt(p),E=r?kt(p):kt(i);return`\n      ${pr(s,a,u===4,4)}\n      fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {\n        ${r?C:w}\n      }\n\n      fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} {\n        ${r?w:C}\n      }\n\n      fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {\n        let col = colIn * ${u};\n        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n        {\n        var value = valueIn;\n        let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n        ${d}\n        ${Gr(n,s)}\n        setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }`}var qg=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Bl(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Vl(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=[\"f32\",\"vec4<f32>\"]):(this.innerElementSize=4,this.variableTypes=[\"vec4<f32>\",\"vec4<f32>\"]),s&&(this.variableNames.push(\"bias\"),this.variableTypes.push(\"vec4<f32>\")),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableTypes.push(\"vec4<f32>\"))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?qu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):ju(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n    ${loe(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n    ${e}\n  `}};var jg=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n       ${pr(this.activation,this.hasPreluActivationWeights,!1,4)}\n       fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n         let coords = vec4<i32>(batch, row, col, chan);\n         if (coordsInBounds4D(coords, uniforms.xShape)) {\n           return  getX(batch, row, col, chan);\n         } else {\n          return 0.0;\n         }\n       }\n       fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n         let coords = vec4<i32>(row, col, xChannel, outChannel);\n         if(coordsInBounds4D(coords, uniforms.wShape)) {\n           return getW(row, col, xChannel, outChannel);\n          } else {\n            return 0.0;\n          }\n       }\n       fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n         let coords = ${this.isChannelsLast?\"vec4<i32>(batch, row, col, chan);\":\"vec4<i32>(batch, chan, row, col);\"}\n         if (coordsInBounds4D(coords, uniforms.outShape)) {\n           var value = valueIn;\n           ${Gr(this.addBias,this.activation)}\n           setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n         }\n       }\n       ${Q(\"index\")} {\n         let coords = getOutputCoords();\n         let batch = coords[0];\n         let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n         let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n         let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n         var acc : f32 = 0.0;\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             let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n             let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n             for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n               ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n               let f = readFilt(row, col, xChannel, outChannel);\n               acc = acc + v * f;\n             }\n           }\n         }\n         writeResult(batch, outRow, outCol, outChannel, acc);\n       }\n     `}};var Xg=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,\n       inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n    ${Q(\"index\")} {\n      let coords = getCoordsFromIndex(index);\n      if(index < uniforms.size) {\n        let batch = coords[0];\n        let row = ${o};\n        let col = ${n};\n        let offsetY = (row / uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0];\n        let xRow = offsetY + uniforms.dilation[0] * (col / uniforms.itemsPerBlockRow);\n        var value = 0.0;\n        if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n          let offsetX = (row % uniforms.outWidth) * uniforms.stride[1] -\n              uniforms.pad[1];\n          let xCol = offsetX + uniforms.dilation[1] * ((col %\n              uniforms.itemsPerBlockRow) / uniforms.inChannels);\n          let ch = col % uniforms.inChannels;\n          if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n            value = ${s};\n          }\n        }\n        setOutputAtIndex(index, value);\n      }\n    }\n   `}};function Yg(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function moe({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=me({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=me({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=me({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=me({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Yg(s.shape,p);x!=null&&(s=me({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Yg(n.shape,p);x!=null&&(n=me({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Xu({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=me({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function doe({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",w=p*u*c,k=h*f,_=C?[t.batchSize,k,w]:[t.batchSize,w,k],E=new Xg(_,C),A=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],R=o.runWebGPUProgram(E,[r],r.dtype,A),D=[];D.push(R);let P=me({inputs:{x:e},backend:o,attrs:{shape:[1,w,-1]}});if(D.push(P),s!=null){let U=Yg(s.shape,C);U!=null&&(s=me({inputs:{x:s},backend:o,attrs:{shape:U}}),D.push(s))}if(n!=null){let U=Yg(n.shape,C);U!=null&&(n=me({inputs:{x:n},backend:o,attrs:{shape:U}}),D.push(n))}let V=Xu({a:C?R:P,b:C?P:R,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=me({inputs:{x:V},backend:o,attrs:{shape:t.outShape}});D.push(V);for(let U of D)o.disposeData(U.dataId);return z}function Qg({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return moe({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(O().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return doe({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new jg(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,E=c?t.outChannels:t.outHeight*t.outWidth,A=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[E]},{type:\"int32\",data:[A]});let R=o.adapterInfo.isIntel();g=new qg(t,_,E,A,p,i,u,R)}let C=[],w=[r,e];p&&(!c&&n.shape.length===1&&(n=me({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),w.push(n)),u&&(!c&&s.shape.length===1&&(s=me({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),w.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,w,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function foe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return Qg({x:n,filter:s,convInfo:m,backend:o})}var oL={kernelName:Uo,backendName:\"webgpu\",kernelFunc:foe};var Zg=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=ae(this.outputShape),this.dispatch=Y(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,o=this.isChannelsLast?3:1;return`\n    ${Q(\"index\")} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${o}];\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 = i32(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 = i32(dyC);\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}},Jg=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pad : vec2<i32>, stride : vec2<i32>, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n    ${Q(\"index\")} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let wR = coords[0];\n        let wC = coords[1];\n        let d1 = coords[2];\n        let d2 = coords[3];\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n          for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n            let xR = wR + yR * uniforms.stride[0] - uniforms.pad[0];\n            if (xR < 0 || xR >= uniforms.inHeight) {\n              continue;\n            }\n\n            for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n              let xC = wC + yC * uniforms.stride[1] - uniforms.pad[1];\n\n              if (xC < 0 || xC >= uniforms.inWidth) {\n                continue;\n              }\n\n              if (${this.isChannelsLast}) {\n                let dyValue = getDy(b, yR, yC, d2);\n                let xValue = getX(b, xR, xC, d1);\n                dotProd = dotProd + xValue * dyValue;\n              } else {\n                let dyValue = getDy(b, d2, yR, yC);\n                let xValue = getX(b, d1, xR, xC);\n                dotProd = dotProd + xValue * dyValue;\n              }\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function hoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Jg(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var nL={kernelName:oi,backendName:\"webgpu\",kernelFunc:hoe};function goe(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n            let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n            let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n            let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n            return vec4<f32>(v0, v1, v2, v3);\n            `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`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 ${kt(r)}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n        return ${kt(r)}(0.0);\n      }\n      let coord = vec4<i32>(\n          batch,\n          i32(xR),\n          i32(xC),\n          col % uniforms.outBackprop[3]);\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n      }\n      return ${kt(r)}(0.0);`;return`\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n    let col = colIn * ${r};\n    ${o}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n    let col = colIn * ${r};\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 rowInner = row % uniforms.outBackprop[3];\n      let coord = vec4<i32>(coordX, coordY, col, rowInner);\n      ${e(r)}\n    }\n    return ${kt(r)}(0.0);\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${kt(r)}) {\n    let col = colIn * ${r};\n    if (row < uniforms.dimAOuter && (col + ${r-1}) < uniforms.dimBOuter) {\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n    }\n  }`}var ex=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,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Bl(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Vl(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=[\"vec4<f32>\",\"f32\"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?qu(this.elementsPerThread,this.workgroupSize):ju(this.elementsPerThread,this.workgroupSize);return`\n    ${goe(this.isVec4?4:1)}\n    ${e}\n    `}};function xoe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.filterHeight<=2&&m.filterWidth<=2&&m.outChannels<=16&&m.inChannels===1)f=new Zg(m);else{f=new ex(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var sL={kernelName:Go,backendName:\"webgpu\",kernelFunc:xoe};var yoe=xe({opType:X.COS}),aL={kernelName:Ho,backendName:\"webgpu\",kernelFunc:yoe};var boe=xe({opType:X.COSH}),iL={kernelName:Ko,backendName:\"webgpu\",kernelFunc:boe};var tx=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=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) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${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    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${o});\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 = ${n};\n        let width_scale = ${i};\n        let in_y = ${s};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${p};\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    `}};var Coe=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new tx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},uL={kernelName:Xo,backendName:\"webgpu\",kernelFunc:Coe};var Zu;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Zu||(Zu={}));var Kl=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Zu.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${pL(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n      ${Q(\"index\")} {\n       if (index < uniforms.size) {\n         var coords = getCoordsFromIndex(index);\n\n         let end = ${cL(e,\"coords\",this.op)};\n         var val = ${o};\n         let pow2 = i32(pow(2.0, uniforms.index));\n         if (${s}) {\n           let idx = ${a};\n           ${cL(e,\"coords\",this.op)} = idx;\n           val ${this.op}= getX(${pL(e,\"coords\",this.op)});\n         }\n         setOutputAtIndex(index, val);\n       }\n      }\n    `}};function pL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function cL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function rx(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=Nr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Kl(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Kl(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=Nr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function Soe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return rx(Zu.Prod,n,t,s,a,i)}var lL={kernelName:qo,backendName:\"webgpu\",kernelFunc:Soe};function woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return rx(Zu.Sum,n,t,s,a,i)}var mL={kernelName:jo,backendName:\"webgpu\",kernelFunc:woe};function Ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=fr({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Tc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var dL={kernelName:ni,backendName:\"webgpu\",kernelFunc:Ioe};var ox=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Q(\"index\")} {\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 = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new ox(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var fL={kernelName:Yo,backendName:\"webgpu\",kernelFunc:voe};var nx=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, inDims : vec2<i32>,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n      ${pr(this.activation,this.hasPreluActivation,!1,4)}\n\n      var<workgroup> mm_Asub : array<array<f32, ${n}>, ${o}>;\n      var<workgroup> mm_Bsub : array<array<f32, ${this.filterWidth}>, ${this.filterHeight}>;\n      fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n        var value = 0.0;\n        if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n        {\n          value = getX(batch, channel, row, col);\n        }\n        return value;\n      }\n\n      ${Q()} {\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.zw) - uniforms.pad;\n        let channelMul = uniforms.wShape[3];\n        let d1 = coords[1] / channelMul;\n        let q = coords[1] % channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n\n        let localRow = i32(localId.y);\n        let localCol = i32(localId.x);\n\n        // Load one tile of X into local memory.\n        for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n          for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n            let rowOffset = inputRow - localRow;\n            let colOffset = inputCol - localCol;\n            mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n          }\n        }\n\n        // Load one tile of W into local memory.\n        var wIndex = i32(localIndex);\n        ${e<t?`if (wIndex < ${e})`:`for(; wIndex < ${e}; wIndex = wIndex + ${t})`}\n\n        {\n          let wRow = wIndex / ${this.filterWidth};\n          let wCol = wIndex % ${this.filterWidth};\n          mm_Bsub[wRow][wCol] = getW(wRow, wCol, d1, q);\n        }\n\n        workgroupBarrier();\n\n        var value = 0.0;\n        for (var wR = 0; wR < ${this.filterHeight}; wR = wR + 1) {\n          for (var wC = 0; wC < ${this.filterWidth}; wC = wC + 1) {\n            let xVal = mm_Asub[localRow + wR][localCol + wC];\n            let wVal = mm_Bsub[wR][wC];\n            value = fma(xVal, wVal, value);\n          }\n        }\n        ${Gr(this.addBias,this.activation)}\n        if (coordsInBounds4D(coords, uniforms.outShape)) {\n          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }\n    `}};var $c=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, inDims : vec2<i32>,\",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n      ${pr(this.activation,this.hasPreluActivation,!0,4)}\n      fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {\n        var value = vec4<f32>(0.0);\n        if (col >=0 && col < uniforms.inDims[1]) {\n          value = getX(batch, row, col, channel);\n        }\n        return value;\n      }\n\n      ${Q()} {\n        let batch = i32(globalId.z) / uniforms.outShape[1];\n        let r = i32(globalId.z) % uniforms.outShape[1];\n        let c = i32(globalId.y) * ${this.workPerThread};\n        let d1 = i32(globalId.x) * 4;\n        let xRCCorner = vec2<i32>(r, c) * vec2<i32>(${t}, ${o}) - uniforms.pad;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n        var xVals : array<vec4<f32>, ${e}>;\n        var dotProd : array<vec4<f32>, ${this.workPerThread}>;\n        for (var i = 0; i < ${this.workPerThread}; i++) {\n          dotProd[i] = vec4<f32>(0.0);\n        }\n\n        // Use constant instead of uniform can give better performance.\n        for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n          let xR = xRCorner + wR;\n          if (xR >=0 && xR < uniforms.inDims[0]) {\n            for (var i = 0; i < ${e}; i++) {\n              xVals[i] = readX(batch, xR, xCCorner + i, d1);\n            }\n            for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n              let wValue = getW(wR, wC, d1, 0);\n              for (var i = 0; i < ${this.workPerThread}; i++) {\n                dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n              }\n            }\n          }\n        }\n\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d1);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            var value = dotProd[i];\n            ${Gr(this.addBias,this.activation)}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n          }\n        }\n      }\n    `}};var Ec=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,\n      filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n      ${pr(this.activation,this.hasPreluActivation,!1,4)}\n\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getOutputCoords();\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.stride - uniforms.pad;\n          let d2 = coords[${this.isChannelsLast?3:1}];\n          let channelMul = uniforms.wShape[3];\n          let d1 = d2 / channelMul;\n          let q = d2 % 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)|x(d1, ?, ?) with w(:, :, d1, q) to get\n          // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n          // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n          // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n          var value = 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              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 = ${e};\n                  let wVal = getW(wR, wC, d1, q);\n                  value = value + 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 = ${e};\n                  let wVal = getW(wR, wC, d1, q);\n                  value = value + xVal * wVal;\n                }\n              }\n            }\n            ${Gr(this.addBias,this.activation)}\n          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }\n    `}};function koe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new nx(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new $c(d):(g=new Ec(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var hL={kernelName:Qo,backendName:\"webgpu\",kernelFunc:koe};var sx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getOutputCoords();\n          let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function Noe(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=me({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new sx(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=me({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var gL={kernelName:si,backendName:\"webgpu\",kernelFunc:Noe};var ax=class{constructor(e){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n       ${Q(\"index\")} {\n         if (index < uniforms.size) {\n           let neg_infinity = -3.4e38;\n           let coords = getOutputCoords();\n           let batch = coords.x;\n           let d1 = coords.w;\n           let outTopLeftCorner = coords.yz * uniforms.stride - uniforms.pad;\n           let hBeg = outTopLeftCorner.x;\n           let wBeg = outTopLeftCorner.y;\n\n           var curVal = neg_infinity;\n           for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n             let hIn = hBeg + h * uniforms.dilation[0];\n\n             if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n               for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n                 let wIn = wBeg + w * uniforms.dilation[1];\n\n                 if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n                   let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n                   if (val > curVal) {\n                     curVal = val;\n                   }\n                 }\n               }\n             }\n           }\n\n           setOutputAtIndex(index, curVal);\n         }\n       }\n     `}};function Toe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new ax(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var xL={kernelName:ai,backendName:\"webgpu\",kernelFunc:Toe};var II=Ze({opType:ge.MUL,cpuKernelImpl:lM,supportsComplex:!0}),yL={kernelName:kn,backendName:\"webgpu\",kernelFunc:II};function ql(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,\"sum\",t)}var bL={kernelName:Kn,backendName:\"webgpu\",kernelFunc:ql};function _oe(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h<l;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:b}=S.getEinsumPermutation(d,p[g]),C;S.isIdentityPermutation(x)?C=s[g]:(C=Nr({inputs:{x:s[g]},backend:t,attrs:{perm:x}}),f.push(C));let w=C.shape.slice();for(let k=0;k<b.length;++k)w.splice(b[k],0,1);y.arraysEqual(C.shape,w)||(C=me({inputs:{x:C},backend:t,attrs:{shape:w}}),f.push(C)),m===null?m=C:(m=II({inputs:{a:C,b:m},backend:t}),f.push(m))}h<l-1&&(u[h]>=0&&(m=ql({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var CL={kernelName:ii,backendName:\"webgpu\",kernelFunc:_oe};var $oe=xe({opType:X.ELU}),SL={kernelName:Jo,backendName:\"webgpu\",kernelFunc:$oe};var Eoe=Ze({opType:ge.EQUAL,dtype:\"bool\",cpuKernelImpl:Q3}),wL={kernelName:en,backendName:\"webgpu\",kernelFunc:Eoe};var Aoe=xe({opType:X.ERF}),IL={kernelName:da,backendName:\"webgpu\",kernelFunc:Aoe};var vI=xe({opType:X.EXP,cpuKernelImpl:Z3,dtype:\"float32\"}),vL={kernelName:tn,backendName:\"webgpu\",kernelFunc:vI};function ix(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),me({inputs:{x:s},backend:o,attrs:{shape:i}})}var kL={kernelName:Ss,backendName:\"webgpu\",kernelFunc:ix};var Roe=xe({opType:X.EXPM1,cpuKernelImpl:J3}),NL={kernelName:fa,backendName:\"webgpu\",kernelFunc:Roe};var jl=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n    fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n      ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n    }\n\n    fn mulMatDFT(batch: i32, index: i32) -> f32 {\n      let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n      let exponentMultiplierTimesIndexRatio =\n          uniforms.exponentMultiplier * indexRatio;\n\n      var result = 0.0;\n\n      for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n        // x = (-2|2 * PI / N) * index * i;\n        let x = exponentMultiplierTimesIndexRatio * f32(i);\n        let expR = cos(x);\n        let expI = sin(x);\n        let real = getReal(batch, i);\n        let imag = getImag(batch, i);\n\n        result = result +\n            unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n      }\n\n      return result;\n    }\n\n    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getOutputCoords();\n        setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n      }\n    }\n  `}};function ux(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=me({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new jl(\"real\",u),l=new jl(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=po({inputs:{real:g,imag:x},backend:t});i.push(b);let C=me({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(w=>t.disposeData(w.dataId)),C}function Doe(r){let{inputs:e,backend:t}=r,{input:o}=e;return ux(o,!1,t)}var TL={kernelName:ui,backendName:\"webgpu\",kernelFunc:Doe};var px=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}};var _L={kernelName:rn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new px(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var Foe=xe({opType:X.FLOOR,cpuKernelImpl:eM}),$L={kernelName:on,backendName:\"webgpu\",kernelFunc:Foe};var Ooe=Ze({opType:ge.INT_DIV,dtype:\"int32\"}),EL={kernelName:nn,backendName:\"webgpu\",kernelFunc:Ooe};var cx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d<f32>\"};\n      ${Q(\"index\")} {\n        let flatIndex = index * uniforms.numChannels;\n        if (flatIndex < uniforms.size) {\n          let coords = getCoordsFromIndex(flatIndex);\n          let values = ${e};\n          for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n            result[flatIndex + i] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}};var AL={kernelName:Zi,backendName:\"webgpu\",kernelFunc:Poe},Ac,kI=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\"),lx=new Map;function Poe(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d){let D=n;if(!lx.has(D)||lx.get(D).expired){let P={source:D};lx.set(D,t.device.importExternalTexture(P))}b={width:c,height:l,format:null,usage:null,texture:lx.get(D)}}else{if(f){let L=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Ac==null||L!==kI)&&(kI=L,Ac=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:kI})),Ac.canvas.width=c,Ac.canvas.height=l,Ac.drawImage(n,0,0,c,l),n=Ac.canvas}let D=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,P=\"rgba8unorm\",M=t.textureManager.acquireTexture(m[1],m[0],P,D);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b={width:c,height:l,format:P,usage:D,texture:M}}let C=y.sizeFromShape(m),w=y.computeStrides(m),k=new cx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...w]}],E=t.makeTensorInfo([l,c],\"int32\"),A=t.tensorMap.get(E.dataId);A.resourceInfo=b;let R=t.runWebGPUProgram(k,[E],\"int32\",_);return t.disposeData(E.dataId),R}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let w=0;w<b;w++)w%4<s&&(g[C++]=h[w])}let x=t.makeTensorInfo(m,\"int32\",new Int32Array(g));return t.uploadToGPU(x.dataId),x}var mx=class{constructor(e,t,o,n,s){this.uniforms=\"varianceEpsilon : f32,\",this.workgroupSize=[128,1,1],this.size=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o),this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\")),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\")),this.offsetShape=n,this.scaleShape=s,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      ${Q(\"index\")} {\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  `}};var RL={kernelName:sn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new mx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function Moe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return Qg({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var DL={kernelName:go,backendName:\"webgpu\",kernelFunc:Moe};function Loe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=S.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],w;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?w=new $c(h,x,m,b):(w=new Ec(h,x,m,b),C.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(C.push({type:\"float32\",data:[d]}),w.uniforms+=\" alpha : f32,\"),t.runWebGPUProgram(w,g,\"float32\",C)}var FL={kernelName:xo,backendName:\"webgpu\",kernelFunc:Loe};var dx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Rt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n      ${Q(\"index\")} {\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 Boe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=S.prepareAndValidate(o,n),m=me({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=me({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=t.readSync(n.dataId),C=t.bufferSync(o),w=tM(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,w.values)}let f=new dx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=me({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var OL={kernelName:an,backendName:\"webgpu\",kernelFunc:Boe};var fx=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Voe(this.aShape);return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${e}));\n        }\n      }\n    `}};function Voe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;o<r.length;o++)o===2?t.push(\"indexZ\"):t.push(`${e[o]}`);return t.join()}function NI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=me({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=me({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])){let C=t.tensorMap.get(d.dataId).values,w=le(d.shape,d.dtype,C),_=t.tensorMap.get(m.dataId).values,E=le(m.shape,m.dtype,_),A=rM(E,w,f);return l.forEach(R=>t.disposeData(R.dataId)),t.makeTensorInfo(u.outputShape,A.dtype,A.values)}let h=new fx(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=me({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var PL={kernelName:Is,backendName:\"webgpu\",kernelFunc:NI};var zoe=Ze({opType:ge.GREATER,cpuKernelImpl:nM,dtype:\"bool\"}),ML={kernelName:un,backendName:\"webgpu\",kernelFunc:zoe};var Woe=Ze({opType:ge.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:oM}),LL={kernelName:pn,backendName:\"webgpu\",kernelFunc:Woe};function Uoe(r){let{inputs:e,backend:t}=r,{input:o}=e;return ux(o,!0,t)}var BL={kernelName:pi,backendName:\"webgpu\",kernelFunc:Uoe};var Goe=xe({opType:X.IS_FINITE,dtype:\"bool\"}),VL={kernelName:ha,backendName:\"webgpu\",kernelFunc:Goe};var Hoe=xe({opType:X.IS_INF,dtype:\"bool\"}),zL={kernelName:ga,backendName:\"webgpu\",kernelFunc:Hoe};var Koe=xe({opType:X.IS_NAN,dtype:\"bool\"}),WL={kernelName:cn,backendName:\"webgpu\",kernelFunc:Koe};function qoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Hr(n.shape,X.LEAKYRELU,\"alpha : f32,\");return t.runWebGPUProgram(i,[n],\"float32\",a)}var UL={kernelName:ln,backendName:\"webgpu\",kernelFunc:qoe};var joe=Ze({opType:ge.LESS,dtype:\"bool\",cpuKernelImpl:aM}),GL={kernelName:mn,backendName:\"webgpu\",kernelFunc:joe};var Xoe=Ze({opType:ge.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:sM}),HL={kernelName:dn,backendName:\"webgpu\",kernelFunc:Xoe};var hx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n        }\n      }\n    `}};function Yoe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new hx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return e.runWebGPUProgram(i,[],\"float32\",p)}var KL={kernelName:li,backendName:\"webgpu\",kernelFunc:Yoe};var Qoe=xe({opType:X.LOG,cpuKernelImpl:iM}),qL={kernelName:fn,backendName:\"webgpu\",kernelFunc:Qoe};var Zoe=xe({opType:X.LOG1P}),jL={kernelName:xa,backendName:\"webgpu\",kernelFunc:Zoe};var Joe=Ze({opType:ge.LOGICAL_AND,dtype:\"bool\"}),XL={kernelName:hn,backendName:\"webgpu\",kernelFunc:Joe};var ene=xe({opType:X.LOGICAL_NOT}),YL={kernelName:gn,backendName:\"webgpu\",kernelFunc:ene};var tne=Ze({opType:ge.LOGICAL_OR}),QL={kernelName:xn,backendName:\"webgpu\",kernelFunc:tne};var ZL=`\n  var powValue = 0.0;\n  let basis = uniforms.bias + uniforms.alpha * sum;\n  if (uniforms.beta == 0.5) {\n    powValue = inverseSqrt(basis);\n  } else if (uniforms.beta == 1.0) {\n    powValue = 1.0 / basis;\n  } else {\n    powValue = exp(log(basis) * (-uniforms.beta));\n  }\n`,gx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getOutputCoords();\n        let b = coords[0];\n        let r = coords[1];\n        let c = coords[2];\n        let d = coords[3];\n\n        let x = getX(b, r, c, d);\n        var sum = 0.0;\n        for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n          let idx = d + i;\n          if (idx >= 0 && idx < uniforms.xShape[3]) {\n            let z = getX(b, r, c, idx);\n            sum = sum + z * z;\n          }\n        }\n        ${ZL}\n\n        setOutputAtIndex(index, x * powValue);\n      }\n    }\n  `}},xx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n    var <workgroup>lrnSub: array<f32, ${this.workgroupSize[0]}>;\n    const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n    const maxAllowRadius = ${this.maxAllowRadius};\n\n    ${Q()} {\n      let localDepth = i32(localId.x);\n      let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n      let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n      let b = i32(globalId.z) / uniforms.xShape[1];\n      let r = i32(globalId.z) - b * uniforms.xShape[1];\n      let c = i32(globalId.y);\n      let d = workgroupDepth + localDepth;\n\n      var x = 0.0;\n      if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n        x = getX(b, r, c, xDepth);\n      }\n      lrnSub[localDepth] = x;\n      workgroupBarrier();\n\n      if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n        var sum = 0.0;\n        let index = localDepth + maxAllowRadius;\n        for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n          let z = lrnSub[index + i];\n          sum = sum + z * z;\n        }\n        ${ZL}\n\n        setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n      }\n    } `}};function rne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new gx(n.shape):u=new xx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var JL={kernelName:mi,backendName:\"webgpu\",kernelFunc:rne};var one=Ze({opType:ge.MAX,cpuKernelImpl:pM}),eB={kernelName:bn,backendName:\"webgpu\",kernelFunc:one};function nne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return zg(n,c,\"max\",t)}var tB={kernelName:Cn,backendName:\"webgpu\",kernelFunc:nne};function sne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,\"min\",t)}var rB={kernelName:wn,backendName:\"webgpu\",kernelFunc:sne};var ane=Ze({opType:ge.MIN,cpuKernelImpl:cM}),oB={kernelName:In,backendName:\"webgpu\",kernelFunc:ane};var yx=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:\"\"}`).join(\",\"),n=e===1?\"start\":\"start[i]\",s=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=Rt(e),p=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let start = ${i}(${t});\n          let end = ${i}(${o});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${e}; i = i + 1) {\n            if (${a} < ${n}) {\n              ${a} = ${n} * 2 - ${a} - ${this.offset};\n            } else if(${a} >= ${s}) {\n              ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${p}));\n        }\n      }\n    `}};var nB={kernelName:vn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var ine=Ze({opType:ge.MOD}),sB={kernelName:ya,backendName:\"webgpu\",kernelFunc:ine};function une(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=mM(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Hr(o.shape,X.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var aB={kernelName:vs,backendName:\"webgpu\",kernelFunc:une};function pne(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var iB={kernelName:Tn,backendName:\"webgpu\",kernelFunc:pne};function cne(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var uB={kernelName:_n,backendName:\"webgpu\",kernelFunc:cne};var bx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if(index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n                                      f32(i32(round(getX(coords.x))) == coords.y)));\n        }\n      }\n    `}};function lne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new bx(u,a),l=me({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=me({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var pB={kernelName:$n,backendName:\"webgpu\",kernelFunc:lne};function Xl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=Xa({inputs:{input:o},backend:t}),s=Xl({inputs:{x:n},backend:t}),a=Qu({inputs:{input:o},backend:t}),i=Xl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return fr({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var cB={kernelName:Os,backendName:\"webgpu\",kernelFunc:Xl};function lB(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=Xa({inputs:{input:o},backend:t}),s=lB({inputs:{x:n},backend:t}),a=Qu({inputs:{input:o},backend:t}),i=Xl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return fr({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var mB={kernelName:ks,backendName:\"webgpu\",kernelFunc:lB};function mne(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return ix({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=ix({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=wI({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var dB={kernelName:Ns,backendName:\"webgpu\",kernelFunc:mne};var Cx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Rt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:\"\"}`).join(\",\"),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?\"any(outC < start)\":\"outC < start\",p=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let start = ${s};\n          let end = ${a};\n          let outC = getCoordsFromIndex(index);\n\n          if (${i} || ${p}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${u}));\n          }\n        }\n      }\n    `}};var TI=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Dt({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return fr({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new Cx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},fB={kernelName:En,backendName:\"webgpu\",kernelFunc:TI};var dne=Ze({opType:ge.POW}),hB={kernelName:An,backendName:\"webgpu\",kernelFunc:dne};function fne(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Yu(ge.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var gB={kernelName:Rn,backendName:\"webgpu\",kernelFunc:fne};function hne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,\"prod\",t)}var xB={kernelName:Dn,backendName:\"webgpu\",kernelFunc:hne};var gne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=hM(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},yB={kernelName:Ts,backendName:\"webgpu\",kernelFunc:gne};var _I=Ze({opType:ge.DIV}),bB={kernelName:Zo,backendName:\"webgpu\",kernelFunc:_I};var xne=xe({opType:X.RECIPROCAL}),CB={kernelName:Fn,backendName:\"webgpu\",kernelFunc:xne};var yne=xe({opType:X.RELU}),SB={kernelName:On,backendName:\"webgpu\",kernelFunc:yne};var bne=xe({opType:X.RELU6}),wB={kernelName:Ln,backendName:\"webgpu\",kernelFunc:bne};var Sx=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Q(\"index\")} {\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 Cne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new Sx(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var IB={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Cne};var wx=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}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      ${Q(\"index\")} {\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 Sne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new wx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var vB={kernelName:Pn,backendName:\"webgpu\",kernelFunc:Sne};var Ix=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4<i32>,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n      \n      // Using uniform variables as judging conditions, so the function has\n      // coherent execution within all threads.\n      fn getReverseCoords(coords : vec4<i32>) -> vec4<i32> {\n        var reverseCoords = coords;\n        if (uniforms.axis[0] == 1) {\n          reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n        }\n        if (uniforms.axis[1] == 1) {\n          reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n        }\n        if (uniforms.axis[2] == 1) {\n          reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n        }\n        if (uniforms.axis[3] == 1) {\n          reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n        }\n\n        return reverseCoords;\n      }\n    \n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let reverseCoords = getReverseCoords(coords);\n          setOutputAtIndex(index, getX(reverseCoords[0],\n              reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n        }\n      }\n    `}};function wne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Dt({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=me({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new Ix(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=me({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var kB={kernelName:Bn,backendName:\"webgpu\",kernelFunc:wne};var vx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(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        ${Q(\"index\")} {\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            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}};var NB={kernelName:ts,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new vx(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Ine=xe({opType:X.ROUND}),TB={kernelName:Vn,backendName:\"webgpu\",kernelFunc:Ine};var vne=xe({opType:X.RSQRT,cpuKernelImpl:gM}),_B={kernelName:zn,backendName:\"webgpu\",kernelFunc:vne};var Hi=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=ae(e),this.dispatch=Y(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Rt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.dispatchLayout.x.length===2&&(n=\"vec2<i32>(flattenedIndex, coords[1])\",s=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        // N.B. |updates| could be a scalar tensor, conceptually representing a\n        // 2D tensor with all values equal to that. By design, its size must be\n        // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n        // gives the other.\n        let sliceSize = uniforms.outShape[1];\n        let d0 = index / sliceSize;\n        let d1 = index - d0 * sliceSize;\n        return vec2<i32>(d0, d1);\n      }\n      `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n    ${s}\n      ${Q(\"index\")} {\n        if (index < uniforms.updatesSize) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${o};\n          }\n          let updateValue =\n              ${vc(this.type,!1)}(${i});\n          let flatIndex = getOutputIndexFromCoords(${n});\n\n          ${this.sumDupeIndices?Ic(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast<i32>(updateValue));\"}\n        }\n      }`}};function kne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=me({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=me({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=fr({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new Hi(f.shape,i,d.shape.length,f.shape.length,c,m,h),w=t.runWebGPUProgram(C,[f,d],h,b,g),k=me({inputs:{x:w},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(w.dataId),k}var $B={kernelName:Wn,backendName:\"webgpu\",kernelFunc:kne};var kx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n      fn findBound(batch: i32, value: f32) -> i32 {\n        var left = i32(0);\n        var right = uniforms.numInputs;\n        while (left < right) {\n          var mid = (left + right) / 2;\n          if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n            left = mid + 1;\n          } else {\n            right = mid;\n          }\n        }\n        return right;\n      }\n\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let value = getValuesByOutputIndex(index);\n          setOutputAtIndexI32(index, findBound(coords[0], value));\n        }\n      }\n    `}};function Nne(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new kx([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var EB={kernelName:fi,backendName:\"webgpu\",kernelFunc:Nne};var Nx=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${n[i]}`),i<this.cRank&&s.push(`${n[i]}`);e=s.join(),t=a.join()}return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function Tne(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new Nx(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var AB={kernelName:$s,backendName:\"webgpu\",kernelFunc:Tne};var _ne=xe({opType:X.SELU}),RB={kernelName:Ca,backendName:\"webgpu\",kernelFunc:_ne};var $ne=xe({opType:X.SIGMOID}),DB={kernelName:Gn,backendName:\"webgpu\",kernelFunc:$ne};var Ene=xe({opType:X.SIGN}),FB={kernelName:wa,backendName:\"webgpu\",kernelFunc:Ene};var Ane=xe({opType:X.SIN}),OB={kernelName:Un,backendName:\"webgpu\",kernelFunc:Ane};var Rne=xe({opType:X.SINH}),PB={kernelName:Sa,backendName:\"webgpu\",kernelFunc:Rne};var $I=Ze({opType:ge.SUB,cpuKernelImpl:wM,supportsComplex:!0}),MB={kernelName:Yn,backendName:\"webgpu\",kernelFunc:$I};function Dne(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Hl({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=me({inputs:{x:i},backend:t,attrs:{shape:p}}),c=$I({inputs:{a:n,b:u},backend:t}),l=vI({inputs:{x:c},backend:t}),m=ql({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=me({inputs:{x:m},backend:t,attrs:{shape:p}}),f=_I({inputs:{a:l,b:d},backend:t});return t.disposeData(i.dataId),t.disposeData(u.dataId),t.disposeData(c.dataId),t.disposeData(l.dataId),t.disposeData(m.dataId),t.disposeData(d.dataId),f}var LB={kernelName:qn,backendName:\"webgpu\",kernelFunc:Dne};var Fne=xe({opType:X.SOFTPLUS}),BB={kernelName:Ia,backendName:\"webgpu\",kernelFunc:Fne};var One=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;x<n.shape.length;++x)p.push([0,0]);let u=[],c=TI({inputs:{x:n},backend:t,attrs:{paddings:p,constantValue:0}}),l=S.getReshaped(c.shape,s,i,!1),m=S.getPermuted(l.length,s.length,!1),d=S.getReshapedPermuted(c.shape,s,i,!1),f=me({inputs:{x:c},backend:t,attrs:{shape:l}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:m}}),g=me({inputs:{x:h},backend:t,attrs:{shape:d}});return u.push(c),u.push(f),u.push(h),u.forEach(x=>t.disposeData(x.dataId)),g},VB={kernelName:As,backendName:\"webgpu\",kernelFunc:One};var Tx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n<o.length;n++)o[n]=e[n]*t[n];this.outputShape=o,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=Pne(this.rank,\"uniforms.\");return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function Pne(r,e=\"\"){if(r>=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n<r;n++)o.push(`(${t[n]} % ${e}aShape[${n}])`);return o.join()}function EI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"||n.shape.length>=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=IM(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Tx(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var zB={kernelName:to,backendName:\"webgpu\",kernelFunc:EI};function Mne(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let A=t.bufferSync(n),R=t.bufferSync(s),D=y.decodeString(t.readSync(a.dataId)[0]),P=xM(A,R,i,m,c,u,p,l,D,d);return t.makeTensorInfo(i,P.dtype,P.values)}let f=[m/c,c],h=me({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?me({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):Dt({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=me({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),w=EI({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let A=new Hi([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(A,[g,h],x,_,w)}break;default:{let A=new Hi([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(A,[b,h],x,_,w)}{let A=new Hi([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(A,[g,h],x,_,w)}}let E=me({inputs:{x:w},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(w.dataId),E}var WB={kernelName:yi,backendName:\"webgpu\",kernelFunc:Mne};function Lne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=fs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var UB={kernelName:Rs,backendName:\"webgpu\",kernelFunc:Lne};var Bne=xe({opType:X.SQRT}),GB={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Bne};var HB={kernelName:bi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Hr(t.shape,X.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Vne=Ze({opType:ge.SQUARED_DIFFERENCE}),KB={kernelName:jn,backendName:\"webgpu\",kernelFunc:Vne};function zne({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Hr(o.shape,X.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var qB={kernelName:fo,backendName:\"webgpu\",kernelFunc:zne};var _x=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Rt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Q(\"index\")} {\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function Wne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=me({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),E=fs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=me({inputs:{x:E},backend:t,attrs:{shape:f}}),t.disposeData(E.dataId)}else if(t.shouldExecuteOnCPU([n])){let E=t.readSync(n.dataId),A=le(n.shape,n.dtype,E),R=CM(d,A,w,b);k=t.makeTensorInfo(f,n.dtype,R.values)}else{let E=new _x(d),A=[{type:\"int32\",data:b},{type:\"int32\",data:w}],R=t.runWebGPUProgram(E,[n],n.dtype,A);k=me({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeData(R.dataId)}return k}var jB={kernelName:Xn,backendName:\"webgpu\",kernelFunc:Wne};function Une(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=SM(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var XB={kernelName:Ds,backendName:\"webgpu\",kernelFunc:Une};var Gne=xe({opType:X.TAN}),YB={kernelName:Qn,backendName:\"webgpu\",kernelFunc:Gne};var Hne=xe({opType:X.TANH}),QB={kernelName:Zn,backendName:\"webgpu\",kernelFunc:Hne};var $x=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(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        ${Q(\"index\")} {\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      `}},Ex=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Q(\"index\")} {\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 Rc(r,e){e!==null&&r.disposeData(e.dataId)}function ZB(r){let e=1;for(;e<r;)e*=2;return e}function Kne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=n.shape,p=i[i.length-1];if(t.shouldExecuteOnCPU([n])){let k=t.readSync(n.dataId),[_,E]=vM(k,i,n.dtype,s,a);return[t.makeTensorInfo(_.shape,_.dtype,_.values),t.makeTensorInfo(E.shape,E.dtype,E.values)]}if(s===0)return i[i.length-1]=0,[t.makeTensorInfo(i,n.dtype,[]),t.makeTensorInfo(i,\"int32\",[])];if(p===1)return[n,fr({attrs:{shape:i,dtype:\"int32\",value:0},backend:t})];let c=y.sizeFromShape(i)/p,l=me({inputs:{x:n},attrs:{shape:[c,p]},backend:t}),m=ZB(s),d=ZB(p),f=null,h=()=>f===null?[l,l]:[l,f],g=(k,_,E)=>{let A=h(),R=new $x(E),P=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(R,A,\"int32\",P),Rc(t,M)};for(let k=1;k<m;k*=2){let _=k*2;for(let E=k;E>=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new Ex([c,k/2]),R=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],D=f;f=t.runWebGPUProgram(E,_,\"int32\",R),Rc(t,D);let P=m/2,M=P*2;for(let L=P;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=fs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),Rc(t,x);let b=NI({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});Rc(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=me({inputs:{x:f},attrs:{shape:C},backend:t}),Rc(t,x);let w=b;return b=me({inputs:{x:b},attrs:{shape:C},backend:t}),Rc(t,w),[b,f]}var JB={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Kne};var Ax=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=ae(this.outputShape),this.dispatch=Y(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          ${Q(\"index\")} {\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 qne(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Ax(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let w=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",w)}var eV={kernelName:es,backendName:\"webgpu\",kernelFunc:qne};function jne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let l=[],m=new Array(i).fill(0),d=a.shape.slice();d[s]=1;let f=new Array(p);for(let h=0;h<f.length;h++){m[s]=h;let g=fs({inputs:{x:a},backend:t,attrs:{begin:m,size:d}}),x=me({inputs:{x:g},backend:t,attrs:{shape:u}});f[h]=x,l.push(g)}return l.forEach(h=>t.disposeData(h.dataId)),f}var tV={kernelName:Fs,backendName:\"webgpu\",kernelFunc:jne};var Xne=[G3,NM,TM,_M,$M,EM,RM,DM,FM,OM,PM,MM,LM,BM,VM,UM,GM,HM,qM,jM,ZM,JM,eL,K3,rL,oL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,CL,SL,wL,IL,vL,kL,NL,TL,W3,_L,AL,$L,EL,RL,DL,FL,OL,PL,ML,LL,H3,BL,tL,VL,zL,WL,UL,GL,HL,KL,jL,qL,XL,YL,QL,JL,zM,eB,tB,WM,rB,oB,nB,sB,yL,aB,iB,uB,XM,pB,mB,dB,fB,hB,gB,xB,yB,YM,bB,CB,SB,wB,U3,IB,vB,kB,NB,TB,_B,$B,EB,AB,RB,DB,FB,OB,PB,KM,qB,jB,XB,LB,BB,VB,WB,UB,GB,HB,KB,MB,bL,YB,QB,zB,JB,eV,AM,tV,cB];for(let r of Xne)ka(r);var rV=\"4.2.0\",Yne=\"4.2.0\",Qne=\"4.2.0\",Zne=\"4.2.0\",Jne=\"4.2.0\",ese=\"0.0.1-alpha.17\",tse={tfjs:rV,\"tfjs-core\":rV,\"tfjs-converter\":Yne,\"tfjs-backend-cpu\":Qne,\"tfjs-backend-webgl\":Zne,\"tfjs-backend-wasm\":Jne,\"tfjs-backend-webgpu\":ese};export{ys as Abs,aa as Acos,ia as Acosh,yu as AdadeltaOptimizer,bu as AdagradOptimizer,Cu as AdamOptimizer,Su as AdamaxOptimizer,eo as Add,Po as AddN,Mo as All,Lo as Any,Bo as ArgMax,ei as ArgMin,ua as Asin,pa as Asinh,ca as Atan,ma as Atan2,la as Atanh,Vo as AvgPool,dp as AvgPool3D,vm as AvgPool3DGrad,mp as AvgPoolGrad,Ll as BackendWasm,zo as BatchMatMul,bs as BatchToSpaceND,ti as Bincount,fp as BroadcastArgs,ise as BroadcastTo,co as Cast,Wo as Ceil,lo as ClipByValue,ri as Complex,hp as ComplexAbs,Cs as Concat,Uo as Conv2D,oi as Conv2DBackpropFilter,Go as Conv2DBackpropInput,gp as Conv3D,km as Conv3DBackpropFilterV2,xp as Conv3DBackpropInputV2,Ho as Cos,Ko as Cosh,Xo as CropAndResize,qo as Cumprod,jo as Cumsum,Fo as DataStorage,ni as DenseBincount,Yo as DepthToSpace,Qo as DepthwiseConv2dNative,yp as DepthwiseConv2dNativeBackpropFilter,bp as DepthwiseConv2dNativeBackpropInput,si as Diag,ai as Dilation2D,$b as Dilation2DBackpropFilter,_b as Dilation2DBackpropInput,kb as ENV,ii as Einsum,Jo as Elu,Nm as EluGrad,Uc as Environment,en as Equal,da as Erf,tn as Exp,Ss as ExpandDims,fa as Expm1,ui as FFT,ws as Fill,rn as FlipLeftRight,on as Floor,nn as FloorDiv,Zi as FromPixels,sn as FusedBatchNorm,go as FusedConv2D,xo as FusedDepthwiseConv2D,Lu as GPGPUContext,an as GatherNd,Is as GatherV2,fl as GraphModel,un as Greater,pn as GreaterEqual,pi as IFFT,mo as Identity,ci as Imag,ha as IsFinite,ga as IsInf,cn as IsNan,Zr as KernelBackend,mi as LRN,Tm as LRNGrad,ln as LeakyRelu,mn as Less,dn as LessEqual,li as LinSpace,fn as Log,xa as Log1p,use as LogSoftmax,hn as LogicalAnd,gn as LogicalNot,xn as LogicalOr,XI as LogicalXor,pse as LowerBound,Pi as MathBackendCPU,Vi as MathBackendWebGL,yn as Max,Cn as MaxPool,Cp as MaxPool3D,$m as MaxPool3DGrad,_m as MaxPoolGrad,Sp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,wu as MomentumOptimizer,wp as Multinomial,kn as Multiply,vs as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,jb as OP_SCOPE_SUFFIX,$n as OneHot,ks as OnesLike,wr as Optimizer,cl as OptimizerConstructors,Ns as Pack,En as PadV2,cse as Pool,An as Pow,Rn as Prelu,Dn as Prod,Iu as RMSPropOptimizer,Ip as RaggedGather,vp as RaggedRange,kp as RaggedTensorToTensor,Ts as Range,Lb as Rank,di as Real,Zo as RealDiv,Fn as Reciprocal,$t as Reduction,On as Relu,Ln as Relu6,_s as Reshape,Mn as ResizeBilinear,Am as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,ts as RotateWithOffset,Vn as Round,zn as Rsqrt,Pa as SGDOptimizer,Wn as ScatterNd,fi as SearchSorted,$s as Select,Ca as Selu,Gn as Sigmoid,wa as Sign,Un as Sin,Sa as Sinh,Es as Slice,qn as Softmax,Ia as Softplus,As as SpaceToBatchND,hi as SparseFillEmptyRows,va as SparseReshape,gi as SparseSegmentMean,xi as SparseSegmentSum,yi as SparseToDense,Rs as SplitV,Hn as Sqrt,bi as Square,jn as SquaredDifference,fo as Step,Xn as StridedSlice,Ds as StringNGrams,Ci as StringSplit,Si as StringToHashBucketFast,Yn as Sub,Kn as Sum,Qn as Tan,Zn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Jn as TopK,es as Transform,ro as Transpose,Np as Unique,Fs as Unpack,Tp as UnsortedSegmentSum,lse as UpperBound,Na as Variable,Gi as WebGPUBackend,Os as ZerosLike,ho as _FusedMatMul,Yt as abs,G0 as acos,H0 as acosh,be as add,K0 as addN,q0 as all,j0 as any,X0 as argMax,Y0 as argMin,Q0 as asin,Z0 as asinh,J0 as atan,ev as atan2,tv as atanh,qm as avgPool,nv as avgPool3d,Pie as backend,S as backend_util,sv as basicLSTMCell,_i as batchNorm,iv as batchNorm2d,uv as batchNorm3d,pv as batchNorm4d,jm as batchToSpaceND,Xm as bincount,VG as booleanMaskAsync,cv as broadcastArgs,$i as broadcastTo,yr as broadcast_util,gN as browser,le as buffer,je as cast,lv as ceil,mv as clipByValue,Lr as clone,Tr as complex,xt as concat,dv as concat1d,fv as concat2d,hv as concat3d,gv as concat4d,xv as conv1d,Ei as conv2d,yv as conv2dTranspose,bv as conv3d,Sv as conv3dTranspose,bse as copyRegisteredKernels,wv as cos,Iv as cosh,al as cosineWindow,vv as cumprod,kv as cumsum,br as customGrad,Nv as denseBincount,nC as deprecationWarn,Tv as depthToSpace,Bp as depthwiseConv2d,Mq as deregisterOp,ki as device_util,_v as diag,$v as dilation2d,kie as disableDeprecationWarnings,Ot as dispose,Nie as disposeVariables,He as div,Av as divNoNan,Rv as dot,ZG as dropout,Dv as einsum,Jm as elu,vie as enableDebugMode,Iie as enableProdMode,CC as enclosingPowerOfTwo,sr as engine,O as env,Zm as equal,Fv as erf,Mv as euclideanNorm,So as exp,Ra as expandDims,Lv as expm1,ed as eye,zp as fft,Us as fill,Fie as findBackend,Oie as findBackendFactory,td as floor,Km as floorDiv,GE as forceHalfFloat,SC as fused,rd as gather,YG as gatherND,Wd as gather_util,Rie as getBackend,Eb as getGradient,Kc as getKernel,Rm as getKernelsForBackend,Kee as getThreadsCount,$w as gpgpu_util,HU as grad,KU as grads,uu as greater,od as greaterEqual,du as ifft,Ri as imag,eK as image,eH as inTopKAsync,La as io,Td as irfft,Bv as isFinite,Vv as isInf,zv as isNaN,_r as keep,Vt as kernel_impls,nd as leakyRelu,Wv as less,Vp as lessEqual,tK as linalg,Uv as linspace,A6 as loadGraphModel,R6 as loadGraphModelSync,Gv as localResponseNormalization,Da as log,sd as log1p,Hv as logSigmoid,Kv as logSoftmax,ud as logSumExp,pu as logicalAnd,pd as logicalNot,cd as logicalOr,qv as logicalXor,rK as losses,jv as lowerBound,Xe as matMul,fN as math,Gs as max,md as maxPool,Xv as maxPool3d,Yv as maxPoolWithArgmax,dd as maximum,cu as mean,Tie as memory,Qv as meshgrid,rl as min,fd as minimum,Zv as mirrorPad,Jv as mod,ek as moments,UG as movingAverage,ne as mul,tk as multiRNNCell,rk as multinomial,Cr as neg,AC as nextFrame,iu as norm,hd as notEqual,nl as oneHot,Hs as ones,ok as onesLike,N as op,nk as outerProduct,Ks as pad,sk as pad1d,ak as pad2d,ik as pad3d,uk as pad4d,pk as pool,Aa as pow,xd as prelu,Hm as print,ck as prod,_ie as profile,lk as raggedGather,mk as raggedRange,dk as raggedTensorToTensor,fk as rand,Ok as randomGamma,Id as randomNormal,Pk as randomStandardNormal,vd as randomUniform,Di as range,Aie as ready,Fa as real,Mk as reciprocal,Ti as registerBackend,gse as registerGradient,ka as registerKernel,Pq as registerOp,Fi as relu,kd as relu6,Die as removeBackend,W as reshape,no as reverse,Lk as reverse1d,Bk as reverse2d,Vk as reverse3d,zk as reverse4d,Wp as rfft,Nd as round,Wk as rsqrt,Ce as scalar,HG as scatterND,sl as scatter_util,ol as searchSorted,Uk as selu,Gk as separableConv2d,oN as serialization,Eie as setBackend,Mie as setPlatform,Hee as setThreadsCount,Uee as setWasmPath,Gee as setWasmPaths,US as setWebGLContext,Hk as setdiff1dAsync,Zp as shared,Ws as sigmoid,Kk as sign,JH as signal,qk as sin,jk as sinh,Ke as slice,Xk as slice1d,Yk as slice2d,Qk as slice3d,Zk as slice4d,ut as slice_util,Jk as softmax,id as softplus,gd as spaceToBatchND,oK as sparse,jG as sparseToDense,ZH as spectral,Oa as split,$r as sqrt,Qt as square,_d as squaredDifference,Up as squeeze,Sr as stack,$d as step,e1 as stridedSlice,nK as string,ke as sub,et as sum,Ta as sumOutType,t1 as tan,tl as tanh,nr as tensor,dr as tensor1d,Oi as tensor2d,Ed as tensor3d,r1 as tensor4d,o1 as tensor5d,n1 as tensor6d,C0 as tensor_util,Fk as test_util,Ee as tidy,Ai as tile,$ie as time,s1 as topk,YMe as train,Gp as transpose,a1 as truncatedNormal,i1 as unique,yse as unregisterGradient,xse as unregisterKernel,u1 as unsortedSegmentSum,so as unstack,dt as upcastType,p1 as upperBound,y as util,qU as valueAndGrad,jU as valueAndGrads,c1 as variable,pC as variableGrads,tse as version,F6 as version_converter,RK as version_core,pj as version_cpu,qee as version_wasm,nY as version_webgl,CJe as webgl,nc as webgl_util,hI as webgpu_util,ss as where,Rd as whereAsync,Br as zeros,Ut as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${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: number[]) => data.reduce((acc: 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 no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\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 size in pixels of a detected face box before resutls are discared */\n  minSize: 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  /** Keep detected faces that cannot be verified using facemesh */\n  keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig 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/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n  /** minimum confidence for a detected race before results are discarded */\n  minConfidence: number,\n}\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  attention: Partial<FaceAttentionConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n  gear: Partial<FaceGearConfig>,\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  /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n  ratio: number,\n  /** possible rvm segmentation mode */\n  mode: SegmentationEnum,\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  /** apply auto-brighness */\n  autoBrightness: 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/**\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: `webgl` for browser and `tensorflow` for nodejs\n  */\n  backend: BackendEnum,\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: false\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: WarmupEnum,\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  /** Validate kernel ops used in model during model load\n   * default: true\n   * any errors will be printed on console but will be treated as non-fatal\n   */\n  validateModels: 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  /** Explicit flags passed to initialize TFJS */\n  flags: Record<string, unknown>,\n\n  /** Software Kernels\n   * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n  */\n  softwareKernels: boolean,\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  validateModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: false,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  flags: {},\n  softwareKernels: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    autoBrightness: 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: false,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      minSize: 0,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      keepInvalid: false,\n    },\n    attention: {\n      enabled: false,\n      modelPath: 'facemesh-attention.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-lite.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: '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: 'rvm.json',\n    ratio: 0.5,\n    mode: 'default',\n  },\n};\n\nexport { config as defaults };\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\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, 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) || 'unknown'}`);\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);\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) || 'unknown'}`);\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) as HTMLCanvasElement;\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  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\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();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\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;\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;\n  }\n\n  const filter = {\n    colorMatrix: (matrix: number[]) => { // 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: number) => {\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: number) => {\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: number) => {\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: number) => {\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: number[]) => { // 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: number) => {\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: number) => {\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: number) => {\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    const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\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 squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const rgb = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n  const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n  // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n  // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n  const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n  const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const maxRange = maxValue > 1 ? 255 : 1;\n  const factor = maxRange / maxValue;\n  let final: Tensor;\n  if (factor > 1) {\n    const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n    const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n    // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n    const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n    const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n    final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n    tf.dispose([...sub, ...range, ...enh]);\n  } else {\n    final = tf.expandDims(squeeze, 0);\n  }\n  tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n  return final;\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, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\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 reset() {\n  last.inputSum = 0;\n  last.cacheDiff = 1;\n  last.sumMethod = 0;\n  last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c: AnyCanvas;\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') {\n        c = document.createElement('canvas');\n        c.width = width;\n        c.height = height;\n      } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\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 react-native without canvas support installed');\n      } else {\n        throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      }\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  // @ts-ignore its either defined or we already threw an error\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: Tensor4D | 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 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 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['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 as Tensor3D, [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 as Tensor4D, [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 as Tensor).shape).toString()}`);\n    if ((tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n  }\n  // check if resizing will be needed\n  if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).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: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n  const originalHeight: number = 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: number = originalWidth;\n  let targetHeight: number = 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 as number;\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 as number;\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?.add) {\n      if (config.debug) log('input process error: cannot initialize filters');\n      env.webgl.supported = false;\n      config.filter.enabled = false;\n      copy(inCanvas, outCanvas); // filter failed to initialize\n      // return { tensor: null, canvas: inCanvas };\n    } else {\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()?.length > 1) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    }\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 as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n    if (env.browser && tf.browser) {\n      pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n    } else {\n      depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n      // const arr = Uint8Array.from(input['data']);\n      const arr = new Uint8Array((input as ImageData).data.buffer);\n      pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).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 as HTMLCanvasElement); // 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 as HTMLCanvasElement);\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: Tensor = tf.cast(pixels, 'float32');\n  const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n  tf.dispose([pixels, casted]);\n\n  if (config.filter.autoBrightness) {\n    const max = tf.max(tensor);\n    const maxVal = await max.data();\n    config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n    tf.dispose(max);\n  }\n\n  return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\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] > 3840 || input.shape[2] > 2160) 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 as Tensor3D, [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  /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n  tensorflow: {\n    version: undefined | string,\n    gpu: undefined | boolean,\n  } = {\n      version: undefined,\n      gpu: undefined,\n    };\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    shader: undefined | string,\n    vendor: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n      shader: undefined,\n      vendor: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | GPUAdapterInfo,\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\n  /** MonkeyPatch for Canvas/Image/ImageData */\n  #canvas: undefined;\n  #image: undefined;\n  #imageData: undefined;\n\n  get Canvas() { return this.#canvas; }\n  set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n  get Image() { return this.#image; }\n  // @ts-ignore monkey-patch;\n  set Image(val) { this.#image = val; globalThis.Image = val; }\n  get ImageData() { return this.#imageData; }\n  // @ts-ignore monkey-patch;\n  set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\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\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n      const agent = navigator.userAgent || '';\n      const raw = agent.match(/\\(([^()]+)\\)/g);\n      if (raw?.[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = agent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\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    try { // backend may not be initialized\n      this.tensorflow = {\n        version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n        gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n      };\n    } catch { /**/ }\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend) {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n    }\n    const c = image.canvas(100, 100);\n    const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n    this.webgl.supported = typeof gl !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && gl) {\n      this.webgl.version = gl.getParameter(gl.VERSION);\n      this.webgl.vendor = gl.getParameter(gl.VENDOR);\n      this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n    }\n    this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      if (this.webgpu.supported) {\n        const adapter = await navigator.gpu.requestAdapter();\n        this.webgpu.adapter = await adapter?.requestAdapterInfo();\n      }\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  updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\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')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\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 } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n  /**\n   * element can be:\n   * - string which indicates dom element id\n   * - actual HTMLVideo dom element\n   * - undefined in which case a new HTMLVideoElement will be created\n   */\n  element: string | HTMLVideoElement | undefined,\n  /** print messages on console */\n  debug: boolean,\n  /** use front or back camera */\n  mode: 'front' | 'back',\n  /** camera crop mode */\n  crop: boolean,\n  /** desired webcam width */\n  width: number,\n  /** desired webcam height */\n  height: number,\n  /** deviceId of the video device to use */\n  id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n  /** current webcam configuration */\n  config: WebCamConfig;\n  /** instance of dom element associated with webcam stream */\n  element: HTMLVideoElement | undefined;\n  /** active webcam stream */\n  stream: MediaStream | undefined;\n  /** enumerated video devices */\n  devices: MediaDeviceInfo[] = [];\n\n  constructor() {\n    this.config = {\n      element: undefined,\n      debug: true,\n      mode: 'front',\n      crop: false,\n      width: 0,\n      height: 0,\n    };\n  }\n\n  /** get active webcam stream track */\n  public get track(): MediaStreamTrack | undefined {\n    if (!this.stream) return undefined;\n    return this.stream.getVideoTracks()[0];\n  }\n\n  /** get webcam capabilities */\n  public get capabilities(): MediaTrackCapabilities | undefined {\n    if (!this.track) return undefined;\n    return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n  }\n\n  /** get webcam constraints */\n  public get constraints(): MediaTrackConstraints | undefined {\n    if (!this.track) return undefined;\n    return this.track.getConstraints ? this.track.getConstraints() : undefined;\n  }\n\n  /** get webcam settings */\n  public get settings(): MediaTrackSettings | undefined {\n    if (!this.stream) return undefined;\n    const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n    return track.getSettings ? track.getSettings() : undefined;\n  }\n\n  /** get webcam label */\n  public get label(): string {\n    if (!this.track) return '';\n    return this.track.label;\n  }\n\n  /** is webcam paused */\n  public get paused(): boolean {\n    return this.element?.paused || false;\n  }\n\n  /** webcam current width */\n  public get width(): number {\n    return this.element?.videoWidth || 0;\n  }\n\n  /** webcam current height */\n  public get height(): number {\n    return this.element?.videoHeight || 0;\n  }\n\n  public enumerate = async (): Promise<MediaDeviceInfo[]> => {\n    try {\n      const devices = await navigator.mediaDevices.enumerateDevices();\n      this.devices = devices.filter((device) => device.kind === 'videoinput');\n    } catch {\n      this.devices = [];\n    }\n    return this.devices;\n  };\n\n  /** start method initializizes webcam stream and associates it with a dom video element */\n  public start = async (webcamConfig?: Partial<WebCamConfig>): Promise<void> => {\n    // set config\n    if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n    if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n    if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n    if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n    if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n    if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n    // use or create dom element\n    if (webcamConfig?.element) {\n      if (typeof webcamConfig.element === 'string') {\n        const el = document.getElementById(webcamConfig.element);\n        if (el && el instanceof HTMLVideoElement) {\n          this.element = el;\n        } else {\n          if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n          return;\n        }\n      } else if (webcamConfig.element instanceof HTMLVideoElement) {\n        this.element = webcamConfig.element;\n      } else {\n        if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n        return;\n      }\n    } else {\n      this.element = document.createElement('video');\n    }\n\n    // set constraints to use\n    const requestedConstraints: MediaStreamConstraints = {\n      audio: false,\n      video: {\n        facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n        // @ts-ignore // resizeMode is still not defined in tslib\n        resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n      },\n    };\n    if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n    if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n    if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n    // set default event listeners\n    this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n    this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n    this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n      if (!this.element || !this.stream) return;\n      if (this.element.paused) await this.element.play();\n      else this.element.pause();\n    });\n\n    // get webcam and set it to run in dom element\n    if (!navigator?.mediaDevices) {\n      if (this.config.debug) log('webcam', 'no devices');\n      return;\n    }\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n    } catch (err) {\n      log('webcam', err);\n      return;\n    }\n    if (!this.stream) {\n      if (this.config.debug) log('webcam', 'no stream');\n      return;\n    }\n    this.element.srcObject = this.stream; // assign it to dom element\n    const ready = new Promise((resolve) => { // wait until stream is ready\n      if (!this.element) resolve(false);\n      else this.element.onloadeddata = () => resolve(true);\n    });\n    await ready;\n    await this.element.play(); // start playing\n\n    if (this.config.debug) {\n      log('webcam', {\n        width: this.width,\n        height: this.height,\n        label: this.label,\n        stream: this.stream,\n        track: this.track,\n        settings: this.settings,\n        constraints: this.constraints,\n        capabilities: this.capabilities,\n      });\n    }\n  };\n\n  /** pause webcam video method */\n  public pause = (): void => {\n    if (this.element) this.element.pause();\n  };\n\n  /** play webcam video method */\n  public play = async (): Promise<void> => {\n    if (this.element) await this.element.play();\n  };\n\n  /** stop method stops active webcam stream track and disconnects webcam */\n  public stop = (): void => {\n    if (this.config.debug) log('webcam', 'stop');\n    if (this.track) this.track.stop();\n  };\n}\n", "{\n  \"antispoof\": 853098,\n  \"blazeface\": 538928,\n  \"centernet\": 4030290,\n  \"emotion\": 820516,\n  \"facemesh\": 1477958,\n  \"faceres\": 6978814,\n  \"handlandmark-lite\": 2023432,\n  \"handtrack\": 2964837,\n  \"iris\": 2599092,\n  \"liveness\": 592976,\n  \"models\": 0,\n  \"movenet-lightning\": 4650216,\n  \"affectnet-mobilenet\": 6920630,\n  \"age\": 161240,\n  \"blazeface-back\": 538928,\n  \"blazeface-front\": 402048,\n  \"blazepose-detector\": 5928856,\n  \"blazepose-full\": 6339202,\n  \"blazepose-heavy\": 27502466,\n  \"blazepose-lite\": 2726402,\n  \"efficientpose\": 5651240,\n  \"faceboxes\": 2013002,\n  \"facemesh-attention-pinto\": 2387598,\n  \"facemesh-attention\": 2382414,\n  \"facemesh-detection-full\": 1026192,\n  \"facemesh-detection-short\": 201268,\n  \"faceres-deep\": 13957620,\n  \"gear-e1\": 112438,\n  \"gear-e2\": 112438,\n  \"gear\": 1498916,\n  \"gender-ssrnet-imdb\": 161236,\n  \"gender\": 201808,\n  \"handdetect\": 3515612,\n  \"handlandmark-full\": 5431368,\n  \"handlandmark-sparse\": 5286322,\n  \"handskeleton\": 5502280,\n  \"meet\": 372228,\n  \"mobileface\": 2183192,\n  \"mobilefacenet\": 5171976,\n  \"movenet-multipose\": 9448838,\n  \"movenet-thunder\": 12477112,\n  \"nanodet\": 7574558,\n  \"posenet\": 5032780,\n  \"rvm\": 3739355,\n  \"selfie\": 212886,\n  \"anti-spoofing\": 853098,\n  \"efficientpose-i-lite\": 2269064,\n  \"efficientpose-ii-lite\": 5651240,\n  \"efficientpose-iv\": 25643252,\n  \"insightface-efficientnet-b0\": 13013224,\n  \"insightface-ghostnet-strides1\": 8093408,\n  \"insightface-ghostnet-strides2\": 8049584,\n  \"insightface-mobilenet-emore\": 6938536,\n  \"insightface-mobilenet-swish\": 12168584,\n  \"nanodet-e\": 12319156,\n  \"nanodet-g\": 7574558,\n  \"nanodet-m\": 1887474,\n  \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n  cacheModels: true,\n  cacheSupported: true,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nexport interface ModelInfo {\n  name: string,\n  inCache: boolean,\n  sizeDesired: number,\n  sizeFromManifest: number,\n  sizeLoadedWeights: number,\n  url: string,\n}\n\nexport const modelStats: Record<string, ModelInfo> = {};\n\nasync function httpHandler(url: string, init?: RequestInit): 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  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n  const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n  modelStats[shortModelName] = {\n    name: shortModelName,\n    sizeFromManifest: 0,\n    sizeLoadedWeights: 0,\n    sizeDesired: modelsDefs[shortModelName],\n    inCache: false,\n    url: '',\n  };\n  options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n  let cachedModels = {};\n  try {\n    cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n  } catch {\n    options.cacheSupported = false;\n  }\n  modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n  let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    if (options.debug) log('model load handler:', model['handler']);\n  } catch (err) {\n    log('error finding model i/o handler:', modelUrl, err);\n  }\n  try {\n    // @ts-ignore private property\n    const artifacts = await model.handler?.load() || null; // load manifest\n    modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n    if (artifacts) model.loadSync(artifacts); // load weights\n    else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n    // @ts-ignore private property\n    modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n    if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      if (options.debug) log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "{\n  \"name\": \"@vladmandic/human\",\n  \"version\": \"3.0.5\",\n  \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n  \"sideEffects\": false,\n  \"main\": \"dist/human.node.js\",\n  \"module\": \"dist/human.esm.js\",\n  \"browser\": \"dist/human.esm.js\",\n  \"types\": \"types/human.d.ts\",\n  \"exports\": {\n    \"node\": {\n      \"require\": \"./dist/human.node.js\",\n      \"import\": \"./dist/human.node.js\",\n      \"module\": \"./dist/human.node.js\"\n    },\n    \"require\": \"./dist/human.node.js\",\n    \"import\": \"./dist/human.esm.js\",\n    \"script\": \"./dist/human.js\",\n    \"module\": \"./dist/human.esm.js\",\n    \"types\": \"./types/human.d.ts\",\n    \"dist/human\": \"./dist/human.js\",\n    \"dist/human.js\": \"./dist/human.js\",\n    \"dist/human.esm\": \"./dist/human.esm.js\",\n    \"dist/human.esm.js\": \"./dist/human.esm.js\",\n    \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n    \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n    \"dist/human.node\": \"./dist/human.node.js\",\n    \"dist/human.node.js\": \"./dist/human.node.js\",\n    \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n    \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n    \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n    \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\"\n  },\n  \"author\": \"Vladimir Mandic <mandic00@live.com>\",\n  \"bugs\": {\n    \"url\": \"https://github.com/vladmandic/human/issues\"\n  },\n  \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n  \"license\": \"MIT\",\n  \"engines\": {\n    \"node\": \">=14.0.0\"\n  },\n  \"repository\": {\n    \"type\": \"git\",\n    \"url\": \"git+https://github.com/vladmandic/human.git\"\n  },\n  \"scripts\": {\n    \"start\": \"node --no-warnings demo/nodejs/node.js\",\n    \"dev\": \"build --profile development\",\n    \"clean\": \"build --profile clean\",\n    \"build\": \"rimraf test/build.log && node build.js\",\n    \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n    \"lint\": \"eslint *.json *.js src demo test models wiki\",\n    \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n  },\n  \"keywords\": [\n    \"human\",\n    \"human-library\",\n    \"face-detection\",\n    \"faceid\",\n    \"face-geometry\",\n    \"face-embedding\",\n    \"face-recognition\",\n    \"face-description\",\n    \"face-matching\",\n    \"body-tracking\",\n    \"body-segmentation\",\n    \"hand-tracking\",\n    \"iris-tracking\",\n    \"age-estimation\",\n    \"emotion-detection\",\n    \"gender-prediction\",\n    \"gesture-recognition\",\n    \"gaze-tracking\",\n    \"age-gender\",\n    \"tensorflowjs\",\n    \"tfjs\",\n    \"tensorflow\"\n  ],\n  \"devDependencies\": {\n    \"@html-eslint/eslint-plugin\": \"^0.16.0\",\n    \"@html-eslint/parser\": \"^0.16.0\",\n    \"@microsoft/api-extractor\": \"^7.34.2\",\n    \"@tensorflow/tfjs-backend-cpu\": \"^4.2.0\",\n    \"@tensorflow/tfjs-backend-wasm\": \"^4.2.0\",\n    \"@tensorflow/tfjs-backend-webgl\": \"^4.2.0\",\n    \"@tensorflow/tfjs-backend-webgpu\": \"0.0.1-alpha.17\",\n    \"@tensorflow/tfjs-converter\": \"^4.2.0\",\n    \"@tensorflow/tfjs-core\": \"^4.2.0\",\n    \"@tensorflow/tfjs-node\": \"^4.2.0\",\n    \"@tensorflow/tfjs-node-gpu\": \"^4.2.0\",\n    \"@types/node\": \"^18.11.18\",\n    \"@types/offscreencanvas\": \"^2019.7.0\",\n    \"@typescript-eslint/eslint-plugin\": \"^5.50.0\",\n    \"@typescript-eslint/parser\": \"^5.50.0\",\n    \"@vladmandic/build\": \"^0.7.15\",\n    \"@vladmandic/pilogger\": \"^0.4.7\",\n    \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n    \"canvas\": \"^2.11.0\",\n    \"esbuild\": \"^0.17.5\",\n    \"eslint\": \"8.33.0\",\n    \"eslint-config-airbnb-base\": \"^15.0.0\",\n    \"eslint-plugin-html\": \"^7.1.0\",\n    \"eslint-plugin-import\": \"^2.27.5\",\n    \"eslint-plugin-json\": \"^3.1.0\",\n    \"eslint-plugin-markdown\": \"^3.0.0\",\n    \"eslint-plugin-node\": \"^11.1.0\",\n    \"eslint-plugin-promise\": \"^6.1.1\",\n    \"rimraf\": \"^4.1.2\",\n    \"tslib\": \"^2.5.0\",\n    \"typedoc\": \"0.23.24\",\n    \"typescript\": \"4.9.5\"\n  }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: null as null | AnyCanvas,\n  gl: null as null | WebGL2RenderingContext,\n  extensions: [] as string[] | null,\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, // default=true\n    desynchronized: true, // default=undefined\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();\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 function register(instance: Human): 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?.getParameter(config.gl.VERSION)) {\n    log('humangl error: backend invalid context');\n    instance.models.reset();\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 = image.canvas(100, 100);\n    } catch (err) {\n      log('humangl error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      if (!config.gl) {\n        log('humangl error: cannot get webgl context');\n        return;\n      }\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('backend 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', (e) => {\n          log('humangl error:', 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        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('humangl error: context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('humangl error: context create:', e);\n        });\n      }\n    } catch (err) {\n      log('humangl error: cannot get webgl context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('humangl error: cannot set webgl context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      // @ts-ignore uncompatible kernelMs timing info\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('humangl 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('humangl error: cannot update webgl backend registration:', err);\n      return;\n    }\n    try {\n      // @ts-ignore private property\n      if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('humangl error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    const backend = tf.backend();\n    const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n    if (current) {\n      if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n    } else {\n      log('humangl error: no current gl context:', current, config.gl);\n    }\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: 255.0,\n  tf1: 1.0,\n  tf2: 2.0,\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(255.0, 'float32');\n  constants.tf1 = tf.scalar(1.0, 'float32');\n  constants.tf2 = tf.scalar(2.0, '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", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise<BackendEnum> {\n  await env.updateBackend(); // update env on backend init\n  if (env.tensorflow?.version) return 'tensorflow';\n  if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n  if (env.webgl.supported && env.webgl.backend) return 'webgl';\n  if (env.wasm.supported && env.wasm.backend) return 'wasm';\n  return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n  const newKernels: string[] = [];\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    newKernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelFloorMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n    };\n    tf.registerKernel(kernelFloorMod);\n    env.kernels.push('floormod');\n    newKernels.push('floormod');\n  }\n  /*\n  if (!env.kernels.includes('atan2') && config.softwareKernels) {\n    const kernelAtan2 = {\n      kernelName: 'Atan2',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => {\n        const backend = tf.getBackend();\n        tf.setBackend('cpu');\n        const t = tf.atan2(op.inputs.a, op.inputs.b);\n        tf.setBackend(backend);\n        return t;\n      }),\n    };\n    if (config.debug) log('registered kernel:', 'atan2');\n    log('registered kernel:', 'atan2');\n    tf.registerKernel(kernelAtan2);\n    env.kernels.push('atan2');\n    newKernels.push('atan2');\n  }\n  */\n  if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n    const kernelRotateWithOffset = {\n      kernelName: 'RotateWithOffset',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => {\n        const backend = tf.getBackend();\n        tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n        const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n        tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n        return t;\n      }),\n    };\n    tf.registerKernel(kernelRotateWithOffset);\n    env.kernels.push('rotatewithoffset');\n    newKernels.push('rotatewithoffset');\n  }\n  if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record<string, unknown> = {};\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\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 = 'webgl';\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 = 'webgl';\n        } else {\n          const adapter = await navigator.gpu.requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n          if (!adapter) {\n            log('override: backend set to webgpu but browser reports no available gpu');\n            instance.config.backend = 'webgl';\n          } else {\n            // @ts-ignore requestAdapterInfo is not in tslib\n            const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n            // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n            log('webgpu adapter info:', adapterInfo);\n          }\n        }\n      }\n\n      // check available backends\n      let available = Object.keys(tf.engine().registryFactory as Record<string, unknown>);\n      if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n        humangl.register(instance);\n        available = Object.keys(tf.engine().registryFactory as Record<string, unknown>);\n      }\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        // @ts-ignore private property\n        if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf.setWasmPaths !== 'undefined') 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        let mt = false;\n        let simd = false;\n        try {\n          mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n          simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\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        } catch {\n          log('wasm detection failed');\n        }\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n      // @ts-ignore private property\n      if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n      // @ts-ignore private property\n      if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false <https://github.com/tensorflow/tfjs/issues/5205>\n      // @ts-ignore private property\n      if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false <https://github.com/tensorflow/tfjs/issues/6678>\n      // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV'])  tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true <https://github.com/tensorflow/tfjs/pull/4909>\n      // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false <https://github.com/tensorflow/tfjs/issues/6687>\n      // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n      // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (instance.config.debug && 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    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    if (instance.config.debug) {\n      // @ts-ignore private property\n      const newFlags = tf.env().flags;\n      const updatedFlags = {};\n      for (const key of Object.keys(newFlags)) {\n        if (defaultFlags[key] === newFlags[key]) continue;\n        updatedFlags[key] = newFlags[key];\n      }\n      if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n    }\n\n    if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n      if (instance.config.debug) log('flags:', instance.config['flags']);\n      for (const [key, val] of Object.entries(instance.config.flags)) {\n        tf.env().set(key, val as number | boolean);\n      }\n    }\n\n    tf.enableProdMode();\n    constants.init();\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend() as BackendEnum;\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps(instance.config);\n    // await env.updateBackend(); // update env on backend init\n    // env.initial = false;\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: 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: (param): TensorInfo => {\n        if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n        return param?.inputs?.info as TensorInfo;\n      },\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 { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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 function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\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?.performance || !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\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n  options.faceLabels = defaultLabels.face;\n  options.bodyLabels = defaultLabels.body;\n  options.bodyPartLabels = defaultLabels.bodyPart;\n  options.handLabels = defaultLabels.hand;\n  options.fingerLabels = defaultLabels.finger;\n  options.objectLabels = defaultLabels.object;\n  options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const 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\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n  const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n  const x = Math.max(0, startX);\n  for (let i = line.length - 1; i >= 0; i--) {\n    const y = i * localOptions.lineHeight + startY;\n    if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n      ctx.fillStyle = localOptions.shadowColor;\n      ctx.fillText(line[i], x + 5, y + 16);\n    }\n    ctx.fillStyle = localOptions.labelColor;\n    ctx.fillText(line[i], x + 4, y + 15);\n  }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  ctx.fillStyle = colorDepth(z, localOptions);\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nexport function 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\nexport function 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    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function 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\nexport function 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", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n  /** draw line color */\n  color: string,\n  /** alpha value used for lines */\n  alpha: number,\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 face attention keypoints be highlighted */\n  drawAttention: 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  /** string template for face labels */\n  faceLabels: string,\n  /** string template for body labels */\n  bodyLabels: string,\n  /** string template for body part labels */\n  bodyPartLabels: string,\n  /** string template for hand labels */\n  handLabels: string,\n  /** string template for hand labels */\n  fingerLabels: string,\n  /** string template for object labels */\n  objectLabels: string,\n  /** string template for gesture labels */\n  gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n  labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n  shadowColor: 'black' as string,\n  alpha: 0.5 as number,\n  font: 'small-caps 16px \"Segoe UI\"' as string,\n  lineHeight: 18 as number,\n  lineWidth: 4 as number,\n  pointSize: 2 as number,\n  roundRect: 8 as number,\n  drawPoints: false as boolean,\n  drawLabels: true as boolean,\n  drawBoxes: true as boolean,\n  drawAttention: true as boolean,\n  drawGestures: true as boolean,\n  drawPolygons: true as boolean,\n  drawGaze: true as boolean,\n  fillPolygons: false as boolean,\n  useDepth: true as boolean,\n  useCurves: false as boolean,\n  faceLabels: '' as string,\n  bodyLabels: '' as string,\n  bodyPartLabels: '' as string,\n  objectLabels: '' as string,\n  handLabels: '' as string,\n  fingerLabels: '' as string,\n  gestureLabels: '' as string,\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], // 11\n  // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n  // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n  // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n  rightEyeIris: [473, 474, 475, 476, 477], // 5\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 irisIndices: { 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] }, // 7 x 3d\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n  { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n  { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\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: 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: 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: 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: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: 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: 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: 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// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const pairsLips: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n  [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n  [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n  [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n  [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n  lips: connectionsToIndices(pairsLips),\n  leftEye: connectionsToIndices(pairsLeftEye),\n  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n  leftIris: connectionsToIndices(pairsLeftIris),\n  rightEye: connectionsToIndices(pairsRightEye),\n  rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n  rightIris: connectionsToIndices(pairsRightIris),\n  faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n  [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n  [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n  [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n  [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n  [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n  [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n  [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n  [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n  [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n  [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n  [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n  [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n  [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n  [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n  [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n  [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n  [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n  [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n  [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n  [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n  [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n  [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n  [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n  [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n  [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n  [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n  [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n  [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n  [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n  [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n  [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n  [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n  [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n  [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n  [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n  [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n  [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n  [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n  [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n  [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n  [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n  [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n  [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n  [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n  [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n  [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n  [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n  [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n  [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n  [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n  [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n  [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n  [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n  [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n  [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n  [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n  [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n  [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n  [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n  [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n  [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n  [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n  [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n  [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 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434], [434, 427], [427, 432],\n  [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n  [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n  [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n  [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n  [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n  [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n  [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n  [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n  [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n  [273, 287], 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426], [426, 322], [322, 436],\n  [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n  [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n  [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n  [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n  [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n  [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n  [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n  [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n  [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n  [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n  [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n  [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n  [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n  [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n  [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n  [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n  [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n  [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], 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466],\n  [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n  [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n  [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n  [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n  [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n  [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n  [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n  [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n  [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n  [5, 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[216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n  [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n  [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n  [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n  [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n  [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n  [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n  [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n  [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n  [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n  [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n  [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n  [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n  [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n  [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n  [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n  [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n  [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n  [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n  [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n  [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n  [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n  [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n  [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n  [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n  [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n  [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n  [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n  [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n  [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n  [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n  [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n  [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n  [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n  [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n  [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n  [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n  [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n  [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n  [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n  [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n  [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n  [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n  [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n  [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n  [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n  [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n  [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n  [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n  [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n  [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n  [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n  [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n  [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n  [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n  [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n  [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n  [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n  [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n  [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n  [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n  [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n  [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n  [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n  [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n  [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n  [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n  [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n  [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n  [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n  [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n  [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n  [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n  [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n  [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n  [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n  [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n  [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n  [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n  [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n  [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n  [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n  [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n  [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n  [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n  [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n  [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n  [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n  [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n  [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n  [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n  [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n  [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n  [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n  [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n  [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n  [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n  [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n  [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n  [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n  [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n  [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n  [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n  [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n  [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n  [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n  [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n  [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n  [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n  [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n  [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n  [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n  [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n  [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n  [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n  [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n  [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n  [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n  [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n  [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n  [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n  [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n  [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n  [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n  [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n  [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n  [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n  [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n  [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n  [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n  [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n  [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n  [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n  [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n  [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n  [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n  [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n  lips: connectionsToIndices(LIPS_CONNECTIONS),\n  leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n  leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n  leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n  rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n  rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n  rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n  faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n  .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n  .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n  indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n  zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n  61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n  185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n  78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n  191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n  76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n  184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n  62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n  183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n  130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n  247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n  226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n  113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n  35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n  143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n  156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n  359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n  467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n  446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n  342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n  265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n  372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n  383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n  468, // Center.\n  469, // Iris right edge.\n  470, // Iris top edge.\n  471, // Iris left edge.\n  472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n  473, // Center.\n  474, // Iris right edge.\n  475, // Iris top edge.\n  476, // Iris left edge.\n  477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n  let l = localOptions.faceLabels.slice();\n  if (f.score) l = replace(l, '[score]', 100 * f.score);\n  if (f.gender) l = replace(l, '[gender]', f.gender);\n  if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n  if (f.age) l = replace(l, '[age]', f.age);\n  if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n  if (f.real) l = replace(l, '[real]', 100 * f.real);\n  if (f.live) l = replace(l, '[live]', 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    l = replace(l, '[emotions]', emotion.join(' '));\n  }\n  if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n  if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n  if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n  if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n  labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (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?.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}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\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}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\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\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (localOptions.drawPolygons && f.mesh.length >= 468) {\n    ctx.lineWidth = 1;\n    for (let i = 0; i < triangulation.length / 3; i++) {\n      const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n      lines(ctx, points, localOptions);\n    }\n    drawIrisElipse(f, ctx);\n  }\n  /*\n  if (localOptions.drawPolygons && f.contours.length > 1) {\n    ctx.lineWidth = 5;\n    lines(ctx, f.contours, opt);\n  }\n  ctx.lineWidth = 1;\n  */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (localOptions.drawPoints) {\n    if (f?.mesh.length >= 468) {\n      for (let i = 0; i < f.mesh.length; i++) {\n        point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n        if (localOptions.drawAttention) {\n          if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n          if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n          if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n        }\n      }\n    } else {\n      for (const [k, v] of Object.entries(f?.annotations || {})) {\n        if (!v?.[0]) continue;\n        const pt = v[0];\n        point(ctx, pt[0], pt[1], 0, localOptions);\n        if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (localOptions.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n  }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial<DrawOptions>) {\n  localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.strokeStyle = localOptions.color;\n  ctx.fillStyle = localOptions.color;\n  for (const f of result) {\n    drawFaceBoxes(f, ctx);\n    drawLabels(f, ctx);\n    if (f.mesh && f.mesh.length > 0) {\n      drawFacePoints(f, ctx);\n      drawFacePolygons(f, ctx);\n      drawGazeSpheres(f, ctx);\n      drawGazeArrows(f, ctx);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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 && (localOptions.bodyLabels?.length > 0)) {\n        let l = localOptions.bodyLabels.slice();\n        l = replace(l, '[score]', 100 * result[i].score);\n        labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\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 = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && 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        let l = localOptions.bodyPartLabels.slice();\n        l = replace(l, '[label]', pt.part);\n        l = replace(l, '[score]', 100 * pt.score);\n        labels(ctx, l, pt.position[0], pt.position[1], localOptions);\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", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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 && (localOptions.handLabels?.length > 0)) {\n        let l = localOptions.handLabels.slice();\n        l = replace(l, '[label]', h.label);\n        l = replace(l, '[score]', 100 * h.score);\n        labels(ctx, l, h.box[0], h.box[1], localOptions);\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 = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n      for (const [part, pt] of Object.entries(h.annotations)) {\n        let l = localOptions.fingerLabels.slice();\n        l = replace(l, '[label]', part);\n        labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n      }\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: 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 = colorDepth(i * z, localOptions);\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", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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 && (localOptions.objectLabels?.length > 0)) {\n        let l = localOptions.objectLabels.slice();\n        l = replace(l, '[label]', h.label);\n        l = replace(l, '[score]', 100 * h.score);\n        labels(ctx, l, h.box[0], h.box[1], localOptions);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n    const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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      const [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        let l = localOptions.gestureLabels.slice();\n        l = replace(l, '[where]', where[0]);\n        l = replace(l, '[who]', who);\n        l = replace(l, '[what]', what[1]);\n        labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n        i += 1;\n      }\n    }\n  }\n}\n", "export const defaultLabels = {\n  face: `face\n    confidence: [score]%\n    [gender] [genderScore]%\n    age: [age] years\n    distance: [distance]cm\n    real: [real]%\n    live: [live]%\n    [emotions]\n    roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n    gaze: [gaze]\u00B0`,\n  body: 'body [score]%',\n  bodyPart: '[label] [score]%',\n  object: '[label] [score]%',\n  hand: '[label] [score]%',\n  finger: '[label]',\n  gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: 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 { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n  const anchors: { 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\nexport async function loadDetector(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n    model = await loadModel(config.body['detector'].modelPath);\n    const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n  } else if (config.debug && model) log('cached model:', model['modelUrl']);\n  createAnchors();\n  return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\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 xMax = tf.add(xMin, width);\n    const yMax = tf.add(yMin, height);\n    const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n    return boxes;\n  });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const detectedBoxes: DetectedBox[] = [];\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n  const nms = await t.nms.data();\n  const scores = await t.scores.data();\n  const boxes = await t.boxes.array();\n  for (const i of Array.from(nms)) {\n    const score = scores[i];\n    const boxRaw: Box = boxes[i];\n    const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n    const detectedBox: DetectedBox = { score, boxRaw, box };\n    detectedBoxes.push(detectedBox);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = model?.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 decodeResults(t.boxes, t.logits, config, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: 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: 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, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 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\nconst cache: BodyResult[] = [];\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 const loadDetect = (config: Config): Promise<GraphModel> => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    model = await loadModel(config.body.modelPath);\n    const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!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 as Tensor4D, [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 as Tensor4D || 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: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): 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    const width = cropBox[2] - cropBox[0];\n    const height = cropBox[3] - cropBox[1];\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] / width + 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\nfunction fixKeypoints(keypoints: 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  if (!model?.['executor']) return null;\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 */] = model?.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: 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, points[depth * i + 1] / inputSize, 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: 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: 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\nexport async function predict(input: Tensor4D, 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    let boxes: DetectedBox[] = [];\n    if (config.body?.['detector']?.['enabled']) {\n      const preparedImage = prepareImage(input, 224);\n      boxes = await detectBoxes(preparedImage, config, outputSize);\n      tf.dispose(preparedImage);\n    } else {\n      boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n    }\n    for (let i = 0; i < boxes.length; i++) {\n      const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n      cache.length = 0;\n      const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n      tf.dispose(preparedBox);\n      if (!bodyResult) continue;\n      bodyResult.id = i;\n      // bodyResult.score = 0; // TBD\n      cache.push(bodyResult);\n    }\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    lastTime = now();\n    skipped = 0;\n  }\n  return 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: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } 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 = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\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: ObjectResult[] = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1); // 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 as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, 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    if (Number.isNaN(classVal)) continue;\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: Tensor4D, config: Config): Promise<ObjectResult[]> {\n  if (!model?.['executor']) return [];\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: 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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\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, Tensor4D } 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): Promise<[number, number, number]> {\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: number = (await max.data())[0]; // get highest score\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, width);\n    const y: number = (await div.data())[0];\n    tf.dispose([reshaped, max, coordinates, mod, div]);\n    return [x, y, newScore];\n  }\n  tf.dispose([reshaped, max]);\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise<BodyResult[]> {\n  if (!model?.['executor'] || !model?.inputs[0].shape) return [];\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      const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\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 = tf.squeeze(resT);\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = tf.unstack(squeeze, 2);\n      tf.dispose(squeeze);\n\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: 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 * 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  const landmarks = box.landmarks;\n  return { startPoint, endPoint, 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 {\n    startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n    endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n    landmarks: box.landmarks,\n    confidence: box.confidence,\n    size,\n  };\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 {\n    startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n    endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n    landmarks: box.landmarks,\n    confidence: box.confidence,\n    size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n  };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return {\n    startPoint: [Math.min(...x), Math.min(...y)] as Point,\n    endPoint: [Math.max(...x), Math.max(...y)] as Point,\n    landmarks,\n  };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => 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: 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: 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: number) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: [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')) {\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[0], centerRaw[1]]);\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 * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, 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['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n  if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\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 as Tensor2D, t.endNormalized as Tensor2D], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise<DetectBox[]> {\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  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) && res.length > 2) { // 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 if (Array.isArray(res)) { // new facemesh-detection tfhub model\n    t.batch = tf.squeeze(res[0]);\n  } else { // original blazeface tfhub model\n    t.batch = tf.squeeze(res);\n  }\n  tf.dispose(res);\n  t.boxes = decodeBoxes(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 as Tensor2D, t.scores as Tensor1D, (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: 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      if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) 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", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\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?.['executor'] && 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 and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.irisIndices.length; i++) {\n    const { key, indices } = coords.irisIndices[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],\n          newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\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: 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.\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, meshSize) {\n  if (!model?.['executor']) return rawCoords;\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, false);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceIrisCoords(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 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    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceIrisCoords(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", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n  const t: Record<string, Float32Array> = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n    // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n    // flag: results[1], // already processed in parent // conv_faceflag\n    lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n    irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n    eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n    irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n    eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n  };\n  for (const val of Object.values(t)) {\n    if (!val) return rawCoords; // could not find tensor\n  }\n\n  // augment iris: adds additional 5 keypoints per eye\n  const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n  const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n  // augment eyes: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n  for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n  // augment lips: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n  return rawCoords;\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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\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: Tensor4D, 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: FaceResult[] = [];\n  const newCache: DetectBox[] = [];\n  let id = 0;\n  const size = inputSize;\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      size: [0, 0],\n      // contoursRaw: [],\n      // contours: [],\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 = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n      tf.dispose(face.tensor);\n      if (equilized) face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, 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.size = box.size;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[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) / size]);\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      if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n        config.face.attention.enabled = false;\n        tf.dispose(face.tensor);\n        return faces;\n      }\n      const results = model.execute(face.tensor as Tensor) as Tensor[];\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const faceConfidence = await confidenceT.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\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        if (config.face.mesh.keepInvalid) {\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] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n          for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n            face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n          }\n        }\n      } else {\n        const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n        const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n        let rawCoords = await coordsReshaped.array();\n        tf.dispose(coordsReshaped);\n        if (config.face.attention?.enabled) {\n          rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n        } else if (config.face.iris?.enabled) {\n          rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n        }\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) / size]);\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 = {\n          ...util.calculateFaceBox(face.mesh, box),\n          confidence: box.confidence,\n          landmarks: box.landmarks,\n          size: box.size,\n        };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        /*\n        const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n        const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n        face.contoursRaw = [];\n        for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n        face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n        */\n        newCache.push(calculatedBox);\n      }\n      tf.dispose(results);\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 (config.face.attention?.enabled && model?.['signature']) {\n    if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n  }\n  if (!model) {\n    if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n    else model = await loadModel(config.face.mesh?.modelPath);\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    model = await loadModel(config.face.emotion?.modelPath);\n    rgb = model?.inputs?.[0].shape?.[3] === 3;\n    if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n    else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ 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: { 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      if (config.face.emotion['crop'] > 0) { // optional crop\n        const crop = config.face.emotion['crop'];\n        const box = [[crop, crop, 1 - crop, 1 - crop]]; // empyrical values for top, left, bottom, right\n        t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      } else {\n        t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      }\n      if (rgb) {\n        t.mul = tf.mul(t.resize, 255);\n        t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n        t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n      } else {\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      }\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 * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\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 Tensor4D; // 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: Tensor = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm: Tensor = 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: Tensor4D, config: Config, idx: number, count: number): Promise<FaceRes> {\n  const obj: FaceRes = {\n    age: 0 as number,\n    gender: 'unknown' as Gender,\n    genderScore: 0 as number,\n    descriptor: [] as number[],\n  };\n  if (!model?.['executor']) return obj;\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 > 0) && (last?.[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\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 = 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) as Tensor1D, 1);\n      const ageIdx: number = (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[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n      if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\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() : [] as 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 type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * 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: { 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: { 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", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: 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: Tensor4D, config: Config, idx: number, count: number): Promise<number> {\n  if (!model?.['executor']) 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 * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: 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: Tensor4D, config: Config, idx: number, count: number): Promise<number> {\n  if (!model?.['executor']) 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", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: 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\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.gear?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor4D, 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", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\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\nexport async function predict(image: Tensor4D, 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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { 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\nexport async function load(config: Config) {\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\nexport async function predict(image: Tensor4D, 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 * MobileFaceNet 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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: 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: Tensor4D, config: Config, idx, count): Promise<number[]> {\n  if (!model?.['executor']) return [];\n  const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n  const skipTime = (config.face['mobilefacenet']?.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: number[] = [];\n    if (config.face['mobilefacenet']?.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      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: 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['insightface'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise<number[]> {\n  if (!model?.['executor']) return [];\n  const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n  const skipTime = (config.face['insightface']?.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: number[] = [];\n    if (config.face['insightface']?.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      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\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    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\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 (Number.isNaN(thetaX)) thetaX = 0;\n    if (Number.isNaN(thetaY)) thetaY = 0;\n    if (Number.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 yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const zAxis = normalize(crossVectors(xAxis, yAxis));\n  // adjust xAxis to make sure that all axes are perpendicular to each other\n  xAxis = crossVectors(yAxis, zAxis);\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    xAxis[0], xAxis[1], xAxis[2],\n    yAxis[0], yAxis[1], yAxis[2],\n    zAxis[0], zAxis[1], zAxis[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", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n  // iris points are [center, left, top, right, bottom]\n  // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n  const f = face?.annotations;\n  if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n  // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n  const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n  // distance of eye from camera in meters\n  const cameraDistance = Math.round(1.17 / irisSize) / 100;\n  return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n  // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n  const f = face?.annotations;\n  if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n  // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n  const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n  // pixel x and y distance of centers of left and right iris, you can use edges instead\n  const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n  // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n  const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n  // distance between eyes in meters\n  const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n  return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\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 * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface 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: Tensor4D): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\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 insightfaceRes: 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: FaceResult[] = [];\n  instance.state = 'run:face';\n  const faces: FaceResult[] = 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      if (masked) faces[i].tensor = masked;\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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || 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 as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || 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 mobilefacenet alternative, 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 as Tensor4D || 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 as Tensor4D || 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 insightface alternative, inherits face from blazeface\n    instance.analyze('Start InsightFace:');\n    if (instance.config.async) {\n      insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End InsightFace:');\n\n    // run faceres, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\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, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, 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 mobilefacenet model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n      (descRes as DescRes).descriptor = insightfaceRes as number[];\n    }\n\n    const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : 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;\n    if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n    if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n    if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n    if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize > 0) res.distance = irisSize;\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 * 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\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\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\n/* eslint-disable camelcase */\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: number[][] = [];\n  const slopesYZ: number[][] = [];\n  const fingerCurls: number[] = [];\n  const fingerDirections: 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: number[] = [];\n    const slopeAtYZ: 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: { 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 * 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: { 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: { 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: { iris: number, gesture: IrisGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations?.leftEyeIris?.[0] || !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: { hand: number, gesture: HandGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: { 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", "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: 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: 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: 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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, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\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: GraphModel) {\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?.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 as Tensor2D, t.endPoints as Tensor2D], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res as Tensor;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\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] ? this.anchors[index] : 0);\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: Tensor4D, config: 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 as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as number[];\n    const hands: { 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] || 1) / this.inputSize, (input.shape[1] || 0) / 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: ({ 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?.inputs?.[0].shape?.[2] || 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\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: { 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: [number, number] = [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 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 * 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: 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 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 = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n  if (handDetector && handPoseModel) 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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } 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\ninterface HandDetectResult {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: HandDetectResult[],\n  hands: 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 = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\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 = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\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: Tensor4D, 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: 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 as Tensor2D, t.max as Tensor1D, (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: Tensor4D, 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: Tensor4D, config: Config): Promise<HandResult[]> {\n  if (!models[0]?.['executor'] || !models[1]?.['executor'] || !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 * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n  /** face id */\n  id: number\n  /** overall face score */\n  score: number,\n  /** detection score */\n  boxScore: number,\n  /** mesh score */\n  faceScore: number,\n  /** detected face box */\n  box: Box,\n  /** detected face box normalized to 0..1 */\n  boxRaw: Box,\n  /** detected face box size */\n  size: [number, number],\n  /** detected face mesh */\n  mesh: Point[]\n  /** detected face mesh normalized to 0..1 */\n  meshRaw: Point[],\n  /** face contours as array of 2d points normalized to 0..1 */\n  // contoursRaw: Array<[number, number]>,\n  /** face contours as array of 2d points */\n  // contours: Array<[number, number]>,\n  /** mesh keypoints combined into annotated results */\n  annotations: Record<FaceLandmark, Point[]>,\n  /** detected age */\n  age?: number,\n  /** detected gender */\n  gender?: Gender,\n  /** gender detection score */\n  genderScore?: number,\n  /** detected emotions */\n  emotion?: { score: number, emotion: Emotion }[],\n  /** detected race */\n  race?: { score: number, race: Race }[],\n  /** face descriptor */\n  embedding?: number[],\n  /** face distance from camera */\n  distance?: number,\n  /** face anti-spoofing result confidence */\n  real?: number,\n  /** face liveness result confidence */\n  live?: number,\n  /** face rotation details */\n  rotation?: {\n    angle: { roll: number, yaw: number, pitch: number },\n    matrix: [number, number, number, number, number, number, number, number, number],\n    gaze: { bearing: number, strength: number },\n  } | null,\n  /** detected face as tensor that can be used in further pipelines */\n  tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n  | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n  | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n  /** body part name */\n  part: BodyLandmark,\n  /** body part position */\n  position: Point,\n  /** body part position normalized to 0..1 */\n  positionRaw: Point,\n  /** body part position relative to body center in meters */\n  distance?: Point,\n  /** body part detection score */\n  score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n  /** body id */\n  id: number,\n  /** body detection score */\n  score: number,\n  /** detected body box */\n  box: Box,\n  /** detected body box normalized to 0..1 */\n  boxRaw: Box,\n  /** detected body keypoints */\n  keypoints: BodyKeypoint[]\n  /** detected body keypoints combined into annotated parts */\n  annotations: Record<BodyAnnotation, Point[][]>,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n  /** hand id */\n  id: number,\n  /** hand overal score */\n  score: number,\n  /** hand detection score */\n  boxScore: number,\n  /** hand skelton score */\n  fingerScore: number,\n  /** detected hand box */\n  box: Box,\n  /** detected hand box normalized to 0..1 */\n  boxRaw: Box,\n  /** detected hand keypoints */\n  keypoints: Point[],\n  /** detected hand class */\n  label: HandType,\n  /** detected hand keypoints combined into annotated parts */\n  annotations: Record<Finger, Point[]>,\n  /** detected hand parts annotated with part gestures */\n  landmarks: Record<Finger, { curl: FingerCurl, direction: FingerDirection }>,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n  | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n  | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n  | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n  | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n  | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n  /** object id */\n  id: number,\n  /** object detection score */\n  score: number,\n  /** detected object class id */\n  class: number,\n  /** detected object class name */\n  label: ObjectType,\n  /** detected object box */\n  box: Box,\n  /** detected object box normalized to 0..1 */\n  boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n  { 'face': number, gesture: FaceGesture }\n  | { 'iris': number, gesture: IrisGesture }\n  | { 'body': number, gesture: BodyGesture }\n  | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n  /** person id */\n  id: number,\n  /** face result that belongs to this person */\n  face: FaceResult,\n  /** body result that belongs to this person */\n  body: BodyResult | null,\n  /** left and right hand results that belong to this person */\n  hands: { left: HandResult | null, right: HandResult | null },\n  /** detected gestures specific to this person */\n  gestures: GestureResult[],\n  /** box that defines the person */\n  box: Box,\n  /** box that defines the person normalized to 0..1 */\n  boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n  /** {@link FaceResult}: detection & analysis results */\n  face: FaceResult[],\n  /** {@link BodyResult}: detection & analysis results */\n  body: BodyResult[],\n  /** {@link HandResult}: detection & analysis results */\n  hand: HandResult[],\n  /** {@link GestureResult}: detection & analysis results */\n  gesture: GestureResult[],\n  /** {@link ObjectResult}: detection & analysis results */\n  object: ObjectResult[]\n  /** global performance object with timing values for each operation */\n  performance: Record<string, number>,\n  /** optional processed canvas that can be used to draw input on screen */\n  canvas?: AnyCanvas | null,\n  /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n  readonly timestamp: number,\n  /** getter property that returns unified persons object  */\n  persons: PersonResult[],\n  /** Last known error message */\n  error: string | null;\n  /** Resolution width */\n  width: number,\n  /** Resolution height */\n  height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: 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: 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: string[][] = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: 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", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } 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 = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return empty();\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\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  */\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,\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 { 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: 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 }; // 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)); // 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]?.[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      let annotations: Record<FaceLandmark, Point[]> = newResult.face[i].annotations;\n      if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n        bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.face[i].annotations;\n      } else if (newResult.face[i].annotations) {\n        for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n          annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n            ? newResult.face[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\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, annotations }; // shallow clone plus updated values\n      } else {\n        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n      }\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  // copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n  // copy resolution info\n  bufferedResult.width = newResult.width;\n  bufferedResult.height = newResult.height;\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", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\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 predict(input: Tensor4D, config: Config): Promise<Tensor | null> {\n  if (!model) model = await load(config);\n  if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n  const t: Record<string, Tensor> = {};\n  t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n  t.squeeze = tf.squeeze(t.res, [0]);\n  // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n  [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n  // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n  t.fg = tf.softmax(t.fgRaw);\n  t.mul = tf.mul(t.fg, constants.tf255);\n  t.expand = tf.expandDims(t.mul, 2);\n  t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n  let rgba: Tensor;\n  switch (config.segmentation.mode || 'default') {\n    case 'default':\n      t.input = tf.squeeze(input);\n      t.concat = tf.concat([t.input, t.output], -1);\n      rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n      break;\n    case 'alpha':\n      rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n      break;\n    default:\n      rgba = tf.tensor(0);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = 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  if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\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 `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: 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) { // 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 = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\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 * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\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 facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n  if (!model) return null;\n  if (!instance?.config?.validateModels) return null;\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n  const ops: string[] = [];\n  const missing: string[] = [];\n  interface Op { name: string, category: string, op: string }\n  const url = model['modelUrl'] as string;\n  const executor = model['executor'];\n  if (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) {\n      log('model not loaded', name);\n    }\n  }\n  for (const op of ops) {\n    if (!simpleOps.includes(op) // exclude simple ops\n      && !ignoreOps.includes(op) // exclude specific 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  if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n  return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n  numLoadedModels: number,\n  numDefinedModels: number,\n  percentageLoaded: number,\n  totalSizeFromManifest: number,\n  totalSizeWeights: number,\n  totalSizeLoading: number,\n  modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n  private instance: Human;\n  models: Record<string, null | GraphModel> = {};\n\n  constructor(currentInstance: Human) {\n    this.models = {};\n    this.instance = currentInstance;\n  }\n\n  stats(): ModelStats {\n    let totalSizeFromManifest = 0;\n    let totalSizeWeights = 0;\n    let totalSizeLoading = 0;\n    for (const m of Object.values(modelStats)) {\n      totalSizeFromManifest += m.sizeFromManifest;\n      totalSizeWeights += m.sizeLoadedWeights;\n      totalSizeLoading += m.sizeDesired;\n    }\n    const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n    return {\n      numLoadedModels: Object.values(modelStats).length,\n      numDefinedModels: Object.keys(this.models).length,\n      percentageLoaded,\n      totalSizeFromManifest,\n      totalSizeWeights,\n      totalSizeLoading,\n      modelStats: Object.values(modelStats),\n    };\n  }\n\n  reset(): void {\n    for (const model of Object.keys(this.models)) this.models[model] = null;\n  }\n\n  async load(instance?: Human): Promise<void> {\n    if (env.initial) this.reset();\n    if (instance) this.instance = instance;\n    const m: Record<string, null | GraphModel | Promise<GraphModel>> = {};\n    // face main models\n    m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n    m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n    m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n    m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n    m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n    m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n    m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n    // face alternatives\n    m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n    m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n    m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n    m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n    m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n    // body alterinatives\n    m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n    m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n    m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n    m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n    m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n    // hand alternatives\n    m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n    m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n    if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n    // object detection alternatives\n    m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n    m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n    // segmentation alternatives\n    m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n    m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n    m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n    // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n    for (const [model, promise] of Object.entries(m)) {\n      if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n    }\n    await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n  }\n\n  list() {\n    const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n    for (const m of models) {\n      const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n      if (!stats) continue;\n      m.size = modelStats[stats].sizeLoadedWeights;\n      m.url = modelStats[stats].url;\n    }\n    return models;\n  }\n\n  loaded() {\n    const list = this.list();\n    const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n    return loaded;\n  }\n\n  validate(): { name: string, missing: string[] }[] {\n    const missing: KernelOps[] = [];\n    for (const defined of Object.keys(this.models)) {\n      const model: GraphModel | null = this.models[defined as keyof Models];\n      if (!model) continue;\n      const res = validateModel(this.instance, model, defined);\n      if (res) missing.push(res);\n    }\n    return missing;\n  }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: 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: BodyKeypoint[]): 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?.[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 as Tensor3D, [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?.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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\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: Box[], // unused\n  bodies: 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?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: 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: 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: 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\nfunction parseMultiPose(res, config, image) {\n  const bodies: 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: 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: 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?.['executor'] || !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      ? parseSinglePose(res, config, input)\n      : 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: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\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, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: 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 = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\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: ObjectResult[] = [];\n  const size = inputSize;\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    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) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n    const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n    const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n    const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n    const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n    const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n    for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n      for (let j = 0; j < (scoresT.shape?.[1] || 0); 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 / (size))); // 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    tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n  }\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: number[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = Array.from(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: Tensor4D, config: Config): Promise<ObjectResult[]> {\n  if (!model?.['executor']) return [];\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 resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const normT = tf.div(resizeT, constants.tf255);\n    const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transposeT);\n    lastTime = now();\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    tf.dispose([resizeT, normT, transposeT, ...objectT]);\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]): 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: 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: number, 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: number, 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: { x: number, y: number }, b: { x: number, y: number }) {\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 * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } 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: number, 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: number, heatmapY: number, heatmapX: number, scores) {\n  const [height, width]: [number, number] = 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: { 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: Tensor4D, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  if (!model?.['executor']) return [];\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: Tensor[] = model.execute(normalized, poseNetOutputs) as 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 = 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]]);\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 * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record<string, Tensor> = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n  tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n  t.r1i = tf.tensor(0.0);\n  t.r2i = tf.tensor(0.0);\n  t.r3i = tf.tensor(0.0);\n  t.r4i = tf.tensor(0.0);\n  ratio = config.segmentation.ratio || 0.5;\n  t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\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  init(config);\n  return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n  const squeeze = tf.squeeze(r, ([0]));\n  const mul = tf.mul(squeeze, constants.tf255);\n  const cast = tf.cast(mul, 'int32');\n  return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n  const rgb = fgr\n    ? normalize(fgr) // normalize and use value\n    : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n  const a = pha\n    ? normalize(pha) // normalize and use value\n    : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n  const rgba = tf.concat([rgb, a], -1);\n  tf.dispose([rgb, a]);\n  return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n  return tf.tidy(() => {\n    const r: Record<string, Tensor | Tensor[]> = {};\n    r.unstack = tf.unstack(state, -1);\n    r.concat = tf.concat(r.unstack, 1);\n    r.split = tf.split(r.concat, 4, 1);\n    r.stack = tf.concat(r.split, 2);\n    r.squeeze = tf.squeeze(r.stack, [0]);\n    r.expand = tf.expandDims(r.squeeze, -1);\n    r.add = tf.add(r.expand, 1);\n    r.mul = tf.mul(r.add, 127.5);\n    r.cast = tf.cast(r.mul, 'int32');\n    r.tile = tf.tile(r.cast, [1, 1, 3]);\n    r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n    return tf.concat([r.tile, r.alpha], -1);\n  });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise<Tensor | null> {\n  if (!model) model = await load(config);\n  if (!model?.['executor']) return null;\n  // const expand = tf.expandDims(input, 0);\n  t.src = tf.div(input, 255);\n  if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n  const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n  let rgba: Tensor;\n  switch (config.segmentation.mode || 'default') {\n    case 'default':\n      rgba = getRGBA(fgr, pha);\n      break;\n    case 'alpha':\n      rgba = getRGBA(null, pha);\n      break;\n    case 'foreground':\n      rgba = getRGBA(fgr, null);\n      break;\n    case 'state':\n      rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n      break;\n    default:\n      rgba = tf.tensor(0);\n  }\n  tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n  return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\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 predict(input: Tensor4D, config: Config): Promise<Tensor | null> {\n  if (!model) model = await load(config);\n  if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n  const t: Record<string, Tensor> = {};\n  t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n  t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n  t.mul = tf.mul(t.alpha, constants.tf255);\n  let rgba: Tensor;\n  switch (config.segmentation.mode || 'default') {\n    case 'default':\n      t.input = tf.squeeze(input);\n      t.concat = tf.concat([t.input, t.mul], -1);\n      rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n      break;\n    case 'alpha':\n      rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n      break;\n    default:\n      rgba = tf.tensor(0);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return rgba;\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: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n  let id = 0;\n  const persons: 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?.[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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob: Blob | null;\n  let res: Result | undefined;\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: string;\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 = '';\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 {\n      resolve(undefined);\n      return;\n    }\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') as CanvasRenderingContext2D;\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, true);\n        const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\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: Result;\n  if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n    // @ts-ignore\n    const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n    const expanded: Tensor = tf.expandDims(data, 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  // @ts-ignore\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\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  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n  // @ts-ignore private property\n  if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n    // log('compile pass: skip');\n    return;\n  }\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  const compiledModels: string[] = [];\n  for (const [modelName, model] of Object.entries(instance.models.models)) {\n    if (!model) continue;\n    const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    try {\n      const res = model.execute(tensor);\n      compiledModels.push(modelName);\n      if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n      else tf.dispose(res);\n    } catch {\n      if (instance.config.debug) log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend['checkCompileCompletionAsync']();\n  webGLBackend['getUniformLocations']();\n  if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  await backend.check(instance, false);\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 empty();\n  }\n  return new Promise(async (resolve) => {\n    await instance.models.load();\n    await runCompile(instance);\n    const res = await runInference(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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\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 rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\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](https://js.tensorflow.org/api/latest/)\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: 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  draw: typeof draw = draw;\n\n  /** Face Matching\n   * - similarity: compare two face descriptors and return similarity index\n   * - distance: compare two face descriptors and return raw calculated differences\n   * - find: compare face descriptor to array of face descriptors and return best match\n   */\n  match: typeof match = match;\n\n  /** Currently loaded models\n   * @internal\n   * {@link models#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  // 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    /*\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    */\n    const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n    defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\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    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\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(this);\n    // reexport draw methods\n    draw.init();\n    this.result = empty();\n    // export access to image processing\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    // init model validation\n    models.validateModel(this, null, '');\n    // include platform info\n    this.emit('create');\n    if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n    if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n    const envTemp = JSON.parse(JSON.stringify(this.env));\n    delete envTemp.kernels;\n    delete envTemp.initial;\n    delete envTemp.perfadd;\n    if (this.config.debug) log('environment:', envTemp);\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    image.reset();\n    env.initial = true;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    const msgs = validate(defaults, userConfig || this.config);\n    if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n    return msgs;\n  }\n\n  /** Utility wrapper for performance.now() */\n  now(): number { // eslint-disable-line class-methods-use-this\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 = false) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns RGBA tensor\n   * Note: Segmentation is not triggered as part of detect process\n   *\n   * @param input - {@link Input}\n   * Returns tensor which contains image data in RGBA format\n   */\n  async segmentation(input: Input, userConfig?: Partial<Config>): Promise<Tensor | null> {\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n    if (!this.config.segmentation.enabled) return null;\n    const processed = await image.process(input, this.config);\n    if (!processed.tensor) return null;\n    let tensor: Tensor | null = null;\n    if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n    if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n    if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n    tf.dispose(processed.tensor);\n    return tensor;\n  }\n\n  /** Compare two input tensors for pixel similarity\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    image.reset();\n  }\n\n  /** WebCam helper methods\n   *\n   */\n  public webcam = new WebCam();\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.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (!await backend.check(this, false)) 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('tf flags:', this.tf.ENV.flags);\n      }\n    }\n\n    await this.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.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      this.models.validate(); // 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 || 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?.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);\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<{ kernel: string, time: number, perc: number }[]> {\n    // @ts-ignore profile wraps method return values\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    let total = 0;\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      const ms = Number(kernel.kernelTimeMs) || 0;\n      if (kernels[kernel.name]) kernels[kernel.name] += ms;\n      else kernels[kernel.name] = ms;\n      total += ms;\n    }\n    const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n    for (const kernel of kernelArr) {\n      kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n      kernel.time = Math.round(1000 * kernel.time) / 1000;\n    }\n    kernelArr.sort((a, b) => b.time - a.time); // sort\n    kernelArr.length = 20; // crop\n    return kernelArr;\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(empty(error));\n      }\n\n      const timeStart = now();\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: Tensor4D };\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(empty('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      this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames)++;\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 || [0, 0, 0, 0];\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        width: shape[2],\n        height: shape[1],\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  /** Helper function\n   * @param ms - sleep time in miliseconds\n   */\n  async sleep(ms: number): Promise<void> { // eslint-disable-line class-methods-use-this\n    return new Promise((resolve) => { setTimeout(resolve, ms); });\n  }\n\n  /** internal structure that keeps track of processed videos @hidden */\n  #loops: Record<string, boolean> = {};\n  /** Continously detect video frames\n   * @param element - HTMLVideoElement input\n   * @param run - boolean run continously or stop if already running, default true\n   * @param delay - number delay detection between frames for number of miliseconds, default 0\n   */\n  async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n    if (run) {\n      if (!this.#loops[element.id]) {\n        if (this.config.debug) log('video start', element.id);\n        this.#loops[element.id] = true;\n      }\n      if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n      if (delay > 0) await this.sleep(delay);\n      if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n    } else {\n      if (this.config.debug) log('video stop', element.id);\n      this.#loops[element.id] = false;\n    }\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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To get the original bytes, call tensor.bytes().\")}return e}async bytes(){this.throwIfDisposed();let e=await ns().read(this.dataId);return this.dtype===\"string\"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(ns().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error(\"Tensor is disposed.\")}print(e=!1){return Dp.print(this,e)}clone(){return this.throwIfDisposed(),Dp.clone(this)}toString(e=!1){let t=this.dataSync();return f0(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Dp.cast(this,e)}variable(e=!0,t,o){return this.throwIfDisposed(),ns().makeVariable(this,e,t,o)}};Object.defineProperty(it,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});function wz(){return Gc(\"Tensor\",()=>it)}wz();var Na=class extends it{constructor(e,t,o,n){super(e.shape,e.dtype,e.dataId,n),this.trainable=t,this.name=o}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Or(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);ns().disposeTensor(this),this.dataId=e.dataId,ns().incRef(this,null)}dispose(){ns().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Na,Symbol.hasInstance,{value:r=>r instanceof it&&r.assign!=null&&r.assign instanceof Function});var C0={};Ge(C0,{assertTypesMatch:()=>Ub,getTensorsInContainer:()=>Qc,isTensorInList:()=>vz,makeTypesMatch:()=>De});var Lb;(function(r){r.R0=\"R0\",r.R1=\"R1\",r.R2=\"R2\",r.R3=\"R3\",r.R4=\"R4\",r.R5=\"R5\",r.R6=\"R6\"})(Lb||(Lb={}));var Bb;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"int32\",r.complex64=\"complex64\"})(Bb||(Bb={}));var Vb;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(Vb||(Vb={}));var zb;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(zb||(zb={}));var Wb;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(Wb||(Wb={}));var Iz={float32:zb,int32:Bb,bool:Vb,complex64:Wb};function dt(r,e){if(r===\"string\"||e===\"string\"){if(r===\"string\"&&e===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${e}`)}return Iz[r][e]}function Ta(r){return dt(r,\"int32\")}function De(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function Ub(r,e){$(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function vz(r,e){return e.some(t=>t.id===r.id)}function Qc(r){let e=[];return b0(r,e,new Set),e}function b0(r,e,t){if(r==null)return;if(r instanceof it){e.push(r);return}if(!kz(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),b0(s,e,t))}}function kz(r){return Array.isArray(r)||typeof r==\"object\"}function Gb(r){return r.kernelName!=null}var Mm=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()}},vi=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Mm}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 o=e[t];if(await this.initializeBackend(o).success){await this.setBackend(o);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,o=1){return e in this.registryFactory?(Ps(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:o},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:o}=this.initializeBackend(e);if(!(o?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new Om(this.backendInstance),!0}setupRegisteredKernels(){Rm(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Rm(e).forEach(o=>{o.disposeFunc!=null&&o.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 o=t.factory();if(o&&!(o instanceof Zr)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,Ps(`Initialization of backend ${e} failed`),Ps(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=o,{success:!0,asyncInit:!1}}catch(o){return Ps(`Initialization of backend ${e} failed`),Ps(o.stack||o.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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 o=e[t],{success:n,asyncInit:s}=this.initializeBackend(o);if(s||n)return{name:o,asyncInit:s}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let o=this.state.tensorInfo.get(t),n=o.backend,s=this.readSync(t),a=n.refCount(t);n.disposeData(t,!0),o.backend=e,e.move(t,s,o.shape,o.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let o=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\");o=e}let n;return this.scopedRun(()=>this.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return vi.nextTensorId++}nextVariableId(){return vi.nextVariableId++}clone(e){let t=T.runKernel(mo,{x:e}),o={x:e},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(co,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(Kc(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Gb(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Gb(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Kc(f,this.backendName);$(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let w=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,w);o=this.saveTensorsForBackwardMode(k)}return w}}else{let{forwardFunc:f}=e,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Gb(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,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Eb(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?($(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=e;o===\"string\"&&Oo(e[0])&&(s=e.map(p=>Ii(p)));let a=n.write(s,t,o),i=new it(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=vb(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||\"float32\";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new it(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new Na(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*up(e.dtype)),this.state.numBytes+=o,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:o})),e instanceof Na||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 o=e.size*up(e.dtype);this.state.numBytes-=o}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,o=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-o;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,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},p=Eb(e);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=lp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,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=Qc(e),o=new Set(t.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let a=this.state.activeScope.track[s];!a.kept&&!o.has(a.id)&&a.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(s=>{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if($(t.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));$(s instanceof it,()=>\"The result y returned by f() must be a tensor.\");let a=l0(this.state.activeTape,t,s);if(!n&&a.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[s.id]=o==null?Nz(s.shape):o,m0(i,a,u=>this.tidy(u),Tz);let p=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(e){return $(gs(e),()=>\"The f passed in customGrad(f) must be a function.\"),(...t)=>{$(t.every(i=>i instanceof it),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),$(o.value instanceof it,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),$(gs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];$(c.length===t.length,()=>\"The function f passed in 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s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=be(ne(p,this.rho),ne(Qt(i),1-this.rho)),l=ne(He($r(be(u,this.epsilon)),$r(be(p,this.epsilon))),i),m=be(ne(u,this.rho),ne(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=be(ne(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var bu=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}static get className(){return\"Adagrad\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Us(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=be(i,Qt(a));i.assign(p);let u=be(ne(He(a,$r(be(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var Cu=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=Ce(t).variable(),this.accBeta2=Ce(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}static get className(){return\"Adam\"}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=ke(1,this.accBeta1),n=ke(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=be(ne(c,this.beta1),ne(u,1-this.beta1)),d=be(ne(l,this.beta2),ne(Qt(u),1-this.beta2)),f=He(m,o),h=He(d,n);c.assign(m),l.assign(d);let g=be(ne(He(f,be($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ne(this.accBeta1,this.beta1)),this.accBeta2.assign(ne(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(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),Ee(()=>{this.accBeta1.assign(Aa(this.beta1,this.iterations_+1)),this.accBeta2.assign(Aa(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}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)}};var Su=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}static get className(){return\"Adamax\"}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=ke(1,this.accBeta1),n=He(-this.learningRate,be(ne(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=be(ne(c,this.beta1),ne(u,1-this.beta1)),d=ne(l,this.beta2),f=Yt(u),h=dd(d,f);c.assign(m),l.assign(h);let g=be(ne(He(n,o),He(m,be(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(ne(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(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)}};var Pa=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}static get className(){return\"SGD\"}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=be(ne(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(Ce(-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 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TypeError(`Node type ${r.op} is not implemented`)}};function Vr(r,e,t=\"\"){if(!(typeof r==\"number\"||typeof e==\"number\")){y.assert(r.length===e.length,()=>t+` Shapes ${r} and ${e} must match`);for(let o=0;o<r.length;o++){let n=r[o],s=e[o];y.assert(n<0||s<0||n===s,()=>t+` Shapes ${r} and ${e} must match`)}}}function PN(r){return!(typeof r==\"number\"||r.some(e=>e<0))}function Hp(r,e,t){let o=of(r,t),n=!PN(o);if(n&&e.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);if(n&&e.forEach(s=>{o=of(s.shape,o)}),!PN(o))throw new Error(`Non-fully-defined elementShape: ${o}`);return o}function of(r,e){if(typeof r==\"number\")return e;if(typeof e==\"number\")return r;if(r.length!==e.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`);let t=[];for(let o=0;o<r.length;++o){let n=r[o],s=e[o];if(n>=0&&s>=0&&n!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`);t[o]=n>=0?n:s}return t}var 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xt(n,0)})}};function MN(r,e,t){let o=r.dtype;if(r.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`);if(r.dtype!==t)throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t}`);let n=r.shape.slice(1);Vr(n,e,\"TensorList shape mismatch: \");let s=so(r);return new Ba(s,e,o)}function LN(r,e,t,o){return new Ba([],r,e,o)}function BN(r,e,t,o){if(e.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);let n=Math.max(...e);if(o!=null&&o!==-1&&n>=o)throw new Error(`Max index must be < array size (${n}  vs. ${o})`);let s=new Ba([],t,r.dtype,o),a=so(r,0);return e.forEach((i,p)=>{s.setItem(i,a[p])}),s}function VN(r,e,t){let o=0,n=e.map(c=>(o+=c,o));if(o!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${o}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=of(s,t),i=o===0?0:r.size/o,p=Ee(()=>{let c=[];r=W(r,[1,o,i]);for(let l=0;l<e.length;++l){let d=[0,l===0?0:n[l-1],0],f=[1,e[l],i];c[l]=W(Ke(r,d,f),a)}return r.dispose(),c}),u=new Ba([],t,r.dtype,e.length);for(let c=0;c<p.length;c++)u.setItem(c,p[c]);return u}var zN=async(r,e,t)=>{switch(r.op){case\"If\":case\"StatelessIf\":{let o=I(\"thenBranch\",r,e,t),n=I(\"elseBranch\",r,e,t),s=I(\"cond\",r,e,t),a=I(\"args\",r,e,t);return(await s.data())[0]?t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case\"While\":case\"StatelessWhile\":{let o=I(\"body\",r,e,t),n=I(\"cond\",r,e,t),s=I(\"args\",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),p=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;p[0];){let c=u;u=await 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o=I(\"size\",r,e,t),n=I(\"dtype\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"dynamicSize\",r,e,t),i=I(\"clearAfterRead\",r,e,t),p=I(\"identicalElementShapes\",r,e,t),u=I(\"name\",r,e,t),c=new nf(u,n,o,s,p,a,i);return t.addTensorArray(c),[c.idTensor,Ce(1)]}case\"TensorArrayWriteV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case\"TensorArrayReadV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"index\",r,e,t);return[t.getTensorArray(o.id).read(n)]}case\"TensorArrayGatherV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"dtype\",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case\"TensorArrayScatterV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case\"TensorArrayConcatV3\":{let 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o=I(\"indices\",r,e,t),n=I(\"tensor\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"numElements\",r,e,t),i=BN(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let o=I(\"elementShape\",r,e,t),n=I(\"elementDType\",r,e,t),s;r.op===\"TensorListReserve\"?s=\"numElements\":s=\"maxNumElements\";let a=I(s,r,e,t),i=r.op===\"TensorListReserve\"?-1:a,p=LN(o,n,a,i);return t.addTensorList(p),[p.idTensor]}case\"TensorListGather\":{let o=I(\"tensorListId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case\"TensorListStack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=MN(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=VN(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[Ce(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function WN(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=ll(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var UN=(r,e,t,o=Ye)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=ll(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=WN(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=WN(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=ll(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=ll(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GN=(r,e,t,o=Ye)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function rS(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var HN=async(r,e,t,o,n=Ye)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=rS(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=rS(r,e,t),c=I(\"padToMaxOutputSize\",r,e,t),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=rS(r,e,t);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case\"Where\":{let s=n.cast(I(\"condition\",r,e,t),\"bool\"),a=[await n.whereAsync(s)];return s.dispose(),a}case\"ListDiff\":return n.setdiff1dAsync(I(\"x\",r,e,t),I(\"y\",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KN=(r,e,t,o=Ye)=>{switch(r.op){case\"LowerBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.lowerBound(n,s)]}case\"TopKV2\":{let n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qN=(r,e,t,o=Ye)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Gt(r.name,e,t)||n];case\"Placeholder\":return[Gt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[is(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>is(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[is(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,u));return[a];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sf=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ce(0),this.tensorMap=new Map,_r(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 Ce(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=so(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i<s;i++){let p=o[i],u=n[i];_r(u),this.tensorMap.set(p,u)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return Ee(()=>{let n=[];for(let s=0;s<o.length;s++){let a=o[s],i=this.findWithDefault(a,t);n.push(i)}return Sr(n)})}findWithDefault(e,t){let o=this.tensorMap.get(e);return o!=null?o: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}`)}};var jN=async(r,e,t,o)=>{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XN=(r,e,t,o=Ye)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YN=(r,e,t,o=Ye)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QN=(r,e,t,o=Ye)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZN=(r,e,t,o=Ye)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];case\"SparseToDense\":return[o.sparseToDense(I(\"sparseIndices\",r,e,t),I(\"outputShape\",r,e,t),I(\"sparseValues\",r,e,t),I(\"defaultValue\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JN=(r,e,t,o=Ye)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eT=(r,e,t,o=Ye)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tT=(r,e,t,o=Ye)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i<n.length;i++)n[i]&&s.push(i);let a=I(\"x\",r,e,t);return[o.reverse(a,s)]}case\"ReverseV2\":{let n=I(\"axis\",r,e,t),s=I(\"x\",r,e,t);return[o.reverse(s,n)]}case\"Slice\":{let n=I(\"begin\",r,e,t),s=I(\"size\",r,e,t);return[o.slice(I(\"x\",r,e,t),n,s)]}case\"StridedSlice\":{let n=I(\"begin\",r,e,t),s=I(\"end\",r,e,t),a=I(\"strides\",r,e,t),i=I(\"beginMask\",r,e,t),p=I(\"endMask\",r,e,t),u=I(\"ellipsisMask\",r,e,t),c=I(\"newAxisMask\",r,e,t),l=I(\"shrinkAxisMask\",r,e,t),m=I(\"x\",r,e,t);return[o.stridedSlice(m,n,s,a,i,p,u,c,l)]}case\"Pack\":return Ee(()=>{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rT=(r,e,t,o=Ye)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oT=(r,e,t,o=Ye)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nT=(r,e,t,o=Ye)=>{switch(r.op){case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sT=(r,e,t,o=Ye)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function oS(r,e,t,o,n=Ee){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>FN(a,i,p));case\"basic_math\":return n(()=>ON(a,i,p));case\"control\":return zN(a,i,p);case\"convolution\":return n(()=>UN(a,i,p));case\"creation\":return n(()=>GN(a,i,p));case\"dynamic\":return HN(a,i,p);case\"evaluation\":return n(()=>KN(a,i,p));case\"image\":return n(()=>XN(a,i,p));case\"graph\":return n(()=>qN(a,i,p));case\"logical\":return n(()=>YN(a,i,p));case\"matrices\":return n(()=>QN(a,i,p));case\"normalization\":return n(()=>ZN(a,i,p));case\"ragged\":return n(()=>JN(a,i,p));case\"reduction\":return n(()=>eT(a,i,p));case\"slice_join\":return n(()=>tT(a,i,p));case\"sparse\":return n(()=>rT(a,i,p));case\"spectral\":return n(()=>oT(a,i,p));case\"string\":return n(()=>nT(a,i,p));case\"transformation\":return n(()=>sT(a,i,p));case\"hash_table\":return jN(a,i,p,o);case\"custom\":let u=Gd(a.op);if(u&&u.customExecutor)return u.customExecutor(new rf(a,i,p));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()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var dl=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,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 o=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(o))}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 nS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=Object.keys(r).map(m=>Ir(m)[0]),c=[];o!=null&&(c=o.map(m=>Ir(m.name)[0]));let l=[...e];for(;l.length>0;){let m=l.pop();if((sS(m)||T6(m)||_6(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function aT(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Ir(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let p=new Set,u=[];for(;s.length>0;){let c=s.pop();p.add(c.name),e[c.name]||u.push(c),c.children.forEach(l=>{!p.has(l.name)&&o.has(l.name)&&l.inputs.every(m=>p.has(m.name))&&s.push(l)})}return u}var v6=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],k6=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],N6=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function sS(r){return v6.indexOf(r.op)>=0}function T6(r){return k6.indexOf(r.op)>=0}function _6(r){return N6.indexOf(r.op)>=0}var ku=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new ku(e.functions[o],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(o=>e[o].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 o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+\"--\"+n.join(this.SEPERATOR)}compile(e,t){let o=nS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),p=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`)}return aT(this.graph,this.weightMap,o)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return _r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(l=>this.graph.nodes[Ir(l)[0]]),s=t.map(l=>Ir(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),p=this.compiledMap.get(i);p==null&&(p=this.compile(e,a),this.compiledMap.set(i,p));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){this.keepIntermediateTensors=!1,console.warn(l.message)}let u={},c={};return Ee(()=>{let l=new dl(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(h=>{let[g,x]=Ir(h),b=[];b[x]=e[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let d=this.getFrozenTensorIds(m),f={};for(let h=0;h<p.length;h++){let g=p[h];if(!m[g.name]){let x=oS(g,m,l,this._resourceManager);if(y.isPromise(x))throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);m[g.name]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[g.name]=this.cloneTensorList(x)),this.checkTensorForDisposal(g.name,g,m,l,d,s,f)}}return this.parent==null&&l.dispose(d),t.map(h=>Gt(h,m,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(o[e].forEach(p=>{p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length)}),t.inputs.forEach(p=>{if(p.category!==\"control\"){let u=EN(p.name,o,n);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new dl(this.weightMap,n,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Gt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(C=>this.graph.nodes[Ir(C)[0]]),i=o.map(C=>Ir(C)[0]),p=i.map(C=>this.graph.nodes[C]);p.length===0&&(p=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:m}=nS(e,p,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(C=>({node:C,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(C=>{let[w,k]=Ir(C),_=[];_[k]=e[C],f[w]=_});let h={},g=this.getFrozenTensorIds(f),x={};for(;d.length>0;){let C=this.processStack(a,d,t,f,x,g,i,h,u);await Promise.all(C)}l==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 b=p.filter(C=>!sS(C)&&!Gt(C.name,f,t)).map(C=>C.name);if(b.length>0){let C=\"\";throw l!=null&&(C=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`)}return f}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=as(l.node.name,o)),n[l.node.name]==null){let d=oS(l.node,n,o,this._resourceManager);m||([m]=as(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=as(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Ir(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Ir(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Ir(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var af=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]}};var $6=\"?tfjs-format=file\",E6=\"model.json\",fl=class{constructor(e,t={},o=La){this.modelUrl=e,this.loadOptions=t,this.version=\"n/a\",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new af}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ku(ml.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=ml.Instance.transformGraph(e.modelInitializer);this.initializer=new ku(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof it?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof it)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count 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t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n<o.length;n++){let s=o[n],a=t[s];this.resourceIdToCapturedInput[a.resourceId]=e[n]}}}execute(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=this.executor.execute(e,t);return o.length>1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return 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Zp={};Ge(Zp,{addImpl:()=>uS,bincountImpl:()=>jp,bincountReduceImpl:()=>uf,castImpl:()=>iS,ceilImpl:()=>pS,concatImpl:()=>Nu,equalImpl:()=>cS,expImpl:()=>mS,expm1Impl:()=>fS,floorImpl:()=>hS,gatherNdImpl:()=>pf,gatherV2Impl:()=>cf,greaterEqualImpl:()=>xS,greaterImpl:()=>gS,lessEqualImpl:()=>bS,lessImpl:()=>yS,linSpaceImpl:()=>lf,logImpl:()=>CS,maxImpl:()=>mf,maximumImpl:()=>SS,minimumImpl:()=>wS,multiplyImpl:()=>hl,negImpl:()=>IS,notEqualImpl:()=>vS,prodImpl:()=>kS,raggedGatherImpl:()=>df,raggedRangeImpl:()=>ff,raggedTensorToTensorImpl:()=>hf,rangeImpl:()=>_u,rsqrtImpl:()=>NS,scatterImpl:()=>Va,sigmoidImpl:()=>OT,simpleAbsImpl:()=>aS,sliceImpl:()=>$u,sparseFillEmptyRowsImpl:()=>gf,sparseReshapeImpl:()=>xf,sparseSegmentReductionImpl:()=>Qp,sqrtImpl:()=>LT,squaredDifferenceImpl:()=>_S,stridedSliceImpl:()=>yf,stringNGramsImpl:()=>Eu,stringSplitImpl:()=>Au,stringToHashBucketFastImpl:()=>Ru,subImpl:()=>ES,tileImpl:()=>bf,topKImpl:()=>Cf,transposeImpl:()=>Xp,uniqueImpl:()=>Sf});function 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No=S.RowPartitionType,Yp=class{constructor(e,t,o,n,s,a,i,p,u,c){this.shape=e,this.shapeShape=t,this.values=o,this.valuesShape=n,this.valuesDType=s,this.defaultValue=a,this.defaultValueShape=i,this.rowPartitionValues=p,this.rowPartitionValuesShapes=u,this.rowPartitionTypes=S.getRowPartitionTypesHelper(c),this.raggedRank=S.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===No.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===No.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){let t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case No.VALUE_ROWIDS:return Yp.getMaxWidthValueRowID(t);case No.ROW_SPLITS:return Yp.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${No[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){let 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firstDimension.\"),s}calculateOutputIndexRowSplit(e,t,o,n){let s=e.length,a=[];for(let i=0;i<s-1;++i){let p=e[i+1]-e[i],u=Math.min(n,p),c=t[i];c===-1&&(u=0);for(let l=0;l<u;++l)a.push(c),c+=o;for(let l=0;l<p-u;++l)a.push(-1)}if(s>0&&a.length!==e[s-1])throw new Error(\"Invalid row split size.\");return a}calculateOutputIndexValueRowID(e,t,o,n){let s=e.length,a=[];if(s===0)return[];let i=0,p=e[0];if(p>=t.length)throw new Error(`Got currentValueRowId=${p}, which is not less than ${t.length}`);let u=t[p];a.push(u);for(let c=1;c<s;++c){let l=e[c];if(l===p)u>=0&&(++i,i<n?u+=o:u=-1);else{if(i=0,p=l,l>=t.length)throw new Error(`Got nextValueRowId=${l} which is not less than ${t.length}`);u=t[l]}a.push(u)}if(a.length!==e.length)throw new Error(\"Invalid row ids.\");return a}calculateOutputIndex(e,t,o,n){let s=this.getRowPartitionTensor(e),a=this.getRowPartitionTypeByDimension(e);switch(a){case No.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,t,o,n);case No.ROW_SPLITS:if(s.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(s,t,o,n);default:throw new Error(`Unsupported partition type: ${No[a]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error(\"No row_partition_types given.\");let t=this.rowPartitionTypes[0];switch(t){case No.FIRST_DIM_SIZE:return e[0];case No.VALUE_ROWIDS:throw new Error(\"Cannot handle VALUE_ROWIDS in first dimension.\");case No.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${No[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error(\"Invalid first partition input. Tensor requires at least one element.\");let t=this.getFirstDimensionSize(),o=this.calculateOutputSize(t),n=new Array(this.raggedRank+1);n[n.length-1]=1;for(let p=n.length-2;p>=0;--p)n[p]=n[p+1]*o[p+1];let s=DT(o,!1),a=y.getArrayFromDType(this.valuesDType,y.sizeFromShape(s));if(n[0]*o[0]>0){let p=this.calculateFirstParentOutputIndex(t,n[0],o[0]);for(let u=1;u<=this.raggedRank;++u)p=this.calculateOutputIndex(u-1,p,n[u],o[u]);this.setOutput(this.raggedRank,p,a,s)}return[s,a]}setOutput(e,t,o,n){if(o.length===0)return;let s=this.values,a=o,i=n.slice();i=i.slice(e+1);let p=y.sizeFromShape(i),u=t.length,c=this.defaultValue;if(c.length!==p&&c.length!==1){let f=this.defaultValueShape;Ee(()=>{let h=W(c,f);c=$i(h,i).dataSync()})}let l=0,m=0,d=0;for(let f=0;f<=u;++f){let h=f<u?t[f]:-1;if(h===d){++d;continue}if(m<d){let g=s.subarray(l*p),x=a.subarray(m*p),b=(d-m)*p;RT(x,g,b)}if(f>=u){let g=o.length;h=Math.floor(g/p)}if(h>d)if(this.defaultValue.length===1)a.subarray(d*p,h*p).fill(this.defaultValue[0]),d=h;else for(;h>d;){let g=a.slice(d*p);RT(g,c,p),++d}h<0?(l=f+1,m=d):(l=f,m=d,d=m+1)}}};function RT(r,e,t){for(let o=0;o<t;o++)r[o]=e[o]}function DT(r,e){let t=[];for(let o of r){if(o<0){if(!e)throw new Error(`Dimension ${o} must be >= 0`);if(o<-1)throw new Error(`Dimension ${o} must be >= -1`);o=-1}t.push(o)}return t}function hf(r,e,t,o,n,s,a,i,p,u){return new Yp(r,e,t,o,n,s,a,i,p,u).compute()}function _u(r,e,t,o){let n=r===e,s=r<e&&t<0,a=e<r&&t>1;if(n||s||a)return y.makeZerosTypedArray(0,o);let i=Math.abs(Math.ceil((e-r)/t)),p=y.makeZerosTypedArray(i,o);e<r&&t===1&&(t=-1),p[0]=r;for(let u=1;u<p.length;u++)p[u]=p[u-1]+t;return p}var NS=vr(r=>1/Math.sqrt(r)),nj=ko(zn,NS),FT={kernelName:zn,backendName:\"cpu\",kernelFunc:nj};function Va(r,e,t,o,n,s,a,i,p,u){let c=[o/n,n],l=r.values,m=e.values;if(o===0)return le(t,e.dtype);let d=le(c,e.dtype);typeof p==\"string\"||typeof p==\"number\"?d.values.fill(p):typeof p==\"boolean\"&&d.values.fill(+p);for(let f=0;f<s;f++){let h=[],g=0;for(let x=0;x<a;x++){let b=l[f*a+x];h.push(b),g+=b*i[x]}if(g<0||g>=o/n)throw new Error(`Invalid indices: ${h} does not index into ${t}`);for(let x=0;x<n;x++)u?d.values[g*n+x]+=m[f*n+x]:d.values[g*n+x]=e.rank===0?m[0]:m[f*n+x]}return d}var OT=vr(r=>1/(1+Math.exp(-r))),TS=we(Gn,r=>1/(1+Math.exp(-r))),PT={kernelName:Gn,backendName:\"cpu\",kernelFunc:TS};function $u(r,e,t,o,n){let s=ut.isSliceContinous(o,e,t),a=y.sizeFromShape(t),i=y.computeStrides(o);if(s){let l=ut.computeFlatOffset(e,i);return n===\"string\"?r.slice(l,l+a):r.subarray(l,l+a)}let p=n===\"string\"?S.fromUint8ToStringArray(r):r,u=le(o,n,p),c=le(t,n);for(let l=0;l<c.size;++l){let m=c.indexToLoc(l),d=m.map((f,h)=>f+e[h]);c.set(u.get(...d),...m)}return n===\"string\"?S.fromStringArrayToUint8(c.values):c.values}function To(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o;j(n,\"slice\");let[i,p]=ut.parseSliceParams(n,s,a);ut.assertParamsValid(n,i,p);let u=t.data.get(n.dataId).values,c=$u(u,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,c)}var MT={kernelName:Es,backendName:\"cpu\",kernelFunc:To};function gf(r,e,t,o,n,s,a){let i=e[0],p=s[0],u=new Array(p),c=new Array(i),l=e[1];if(p===0){if(i!==0)throw new Error(S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));let g=y.getArrayFromDType(t,0),x=y.getArrayFromDType(n,0);return[g,[0,l],x,u,c]}let m=!0,d=0,f=new Array(p).fill(0);for(let g=0;g<i;++g){let x=r[g*l];if(x<0)throw new Error(S.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,x));if(x>=p)throw new Error(S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,x,p));++f[x],m=m&&x>=d,d=x}let h=!0;for(let g=0;g<p;++g){let x=f[g]===0;u[g]=x,h=h&&!x,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(h&&m){let g=r,x=o;for(let b=0;b<i;++b)c[b]=b;return[g,[i,l],x,u,c]}else{let 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m=o.length,d=[];if(m>0){d[m-1]=1;for(let g=m-2;g>=0;--g)d[g]=d[g+1]*o[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*p[g+1]}let h=y.getArrayFromDType(t,a*i);for(let g=0;g<a;++g){let x=0;for(let b=0;b<m;++b)x+=r[g*m+b]*d[b];for(let b=0;b<i;++b)h[g*i+b]=Math.trunc(x/f[b]),x%=f[b]}return[h,[a,i],p]}function Qp(r,e,t,o,n,s=!1,a=0){let i=o.length,p=[e[0],r.length/e[0]],u=p[1],l=i>0?n[i-1]+1:0;if(l<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=e.slice();m[0]=l;let d=m.reduce((C,w)=>C*w,1),f=y.getArrayFromDType(t,d);if(i===0)return l>0&&f.fill(a),[f,m];if(l<=0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let h=0,g=1,x=0,b=n[h];for(;;){let C=0;if(g<i){if(C=n[g],b===C){++g;continue}if(b>=C)throw new Error(S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(b<0||b>=l)throw new Error(S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b,l));b>x&&f.fill(a,x*u,b*u);for(let 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R2={kernelName:jo,backendName:\"cpu\",kernelFunc:Gj};function Hj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.data.get(n.dataId).values,u=t.data.get(s.dataId).values,c=jp(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=uf(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var D2={kernelName:ni,backendName:\"cpu\",kernelFunc:Hj};function Kj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(a===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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P2={kernelName:yp,backendName:\"cpu\",kernelFunc:qj};function jj(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o;j([n,s],\"depthwiseConv2DNativeBackpropInput\");let l=y.computeStrides(n.shape),m=y.computeStrides(s.shape),d=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),f=new st(d.inShape,\"float32\"),h=f.values,[g,x,b]=f.strides,C=t.data.get(n.dataId).values,[w,k,_]=l,E=t.data.get(s.dataId).values,[A,R,D]=m,{batchSize:P,filterHeight:M,filterWidth:L,inChannels:V,inHeight:z,inWidth:U,outChannels:K,outHeight:H,outWidth:q,strideHeight:Z,strideWidth:ee}=d,oe=M-1-d.padInfo.top,J=L-1-d.padInfo.left,te=K/V;for(let ie=0;ie<P;++ie)for(let ce=0;ce<V;++ce)for(let ye=0;ye<z;++ye){let Ne=ye-oe,Ie=Math.max(0,Math.ceil(Ne/Z)),Ae=Math.min(H,(M+Ne)/Z);for(let Re=0;Re<U;++Re){let ot=Re-J,pt=Math.max(0,Math.ceil(ot/ee)),ze=Math.min(q,(L+ot)/ee),ct=0;for(let at=Ie;at<Ae;++at){let ft=at*Z-Ne;for(let ht=pt;ht<ze;++ht){let 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Se=O();Se.registerFlag(\"HAS_WEBGL\",()=>Se.getNumber(\"WEBGL_VERSION\")>0);Se.registerFlag(\"WEBGL_VERSION\",()=>Df(2)?2:Df(1)?1:0);Se.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);Se.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>Se.get(\"WEBGL_VERSION\")===2);Se.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);Se.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);Se.registerFlag(\"WEBGL_PACK\",()=>Se.getBool(\"HAS_WEBGL\"));Se.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_CLIP\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_PACK_REDUCE\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_CONV_IM2COL\",()=>Se.getBool(\"WEBGL_PACK\"));Se.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>aw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>iw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let r=Se.getNumber(\"WEBGL_VERSION\");return r===0?0:uw(r)});Se.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>Se.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!ki.isMobile());Se.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>pw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>Se.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:Se.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));Se.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>cw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>lw(Se.getNumber(\"WEBGL_VERSION\")));Se.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>Se.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);Se.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Se.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>ki.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Se.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);Se.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);Se.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);Se.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);Se.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);Se.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>Se.getBool(\"IS_TEST\"));Se.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);Se.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);Se.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);Se.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function wt(){let r,e,t,o,n,s,a,i,p,u;return O().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",e=\"in\",t=\"out\",o=\"in\",n=\"texture\",s=\"outputColor\",a=\"out vec4 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${t} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${a}; ${i};`}).join(\"\")}function s8(r,e){let t=r.length,o=r.map(s=>`${e}[${s}]`),n=new Array(t-1);n[t-2]=o[t-1];for(let s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}function A$(r,e,t=\"index\"){let o=r.map((s,a)=>a),n=s8(o,e);return n.map((s,a)=>{let i=`int ${r[a]} = ${t} / ${n[a]}`,p=a===n.length-1?`int ${r[a+1]} = ${t} - ${r[a]} * ${n[a]}`:`index -= ${r[a]} * ${n[a]}`;return`${i}; ${p};`}).join(\"\")}function sc(r){let e=y.computeStrides(r).map(t=>t.toString());return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;\n  }\n`}function ac(){return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`}var Ff=`\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;var{getBroadcastDims:R$}=S;function D$(r,e,t){let o=[];if(r.forEach(d=>{let f=y.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${f>1?`[${f}]`:\"\"};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`)),t.enableShapeUniforms){let{uniformShape:h}=Of(t.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(h.length){case 1:o.push(`uniform int ${d.name}Shape;`);break;case 2:o.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:o.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:o.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}o.push(`uniform ivec2 ${d.name}TexShape;`)}}),t.enableShapeUniforms){switch(e.logicalShape.length){case 1:o.push(\"uniform int outShape;\");break;case 2:o.push(\"uniform ivec2 outShape;\"),o.push(\"uniform int outShapeStrides;\");break;case 3:o.push(\"uniform ivec3 outShape;\"),o.push(\"uniform ivec2 outShapeStrides;\");break;case 4:o.push(\"uniform ivec4 outShape;\"),o.push(\"uniform ivec3 outShapeStrides;\");break;default:break}o.push(\"uniform ivec2 outTexShape;\")}t.customUniforms&&t.customUniforms.forEach(d=>{o.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:\"\"};`)});let n=o.join(`\n`),s=r.map(d=>a8(d,e,t.packedInputs,t.enableShapeUniforms)).join(`\n`),a=e.texShape,i=wt(),p=p8(i),u,c,l=m8(i);return e.isPacked?(u=i8(e.logicalShape,a,t.enableShapeUniforms),c=l8(i)):(u=u8(e.logicalShape,a,t.enableShapeUniforms),c=c8(i)),t.packedInputs&&(l+=g8),[l,p,c,n,u,s,t.userCode].join(`\n`)}function uc(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return _8(r,e);case 1:return E8(r,e);case 2:return R8(r,e);case 3:return F8(r,e);case 4:return P8(r,e);case 5:return M8(r);case 6:return L8(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function F$(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return T8(r);case 1:return $8(r,e);case 2:return A8(r,e);case 3:return D8(r,e);default:return O8(r,e)}}function a8(r,e,t=!1,o){let n=\"\";t?n+=F$(r,o):n+=uc(r,o);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?n+=B8(r,e):n+=V8(r,e)),n}function i8(r,e,t){switch(r.length){case 0:return O$();case 1:return x8(r,e,t);case 2:return k8(r,e,t);case 3:return b8(r,e,t);default:return S8(r,e,t)}}function u8(r,e,t){switch(r.length){case 0:return O$();case 1:return y8(r,e,t);case 2:return N8(r,e,t);case 3:return C8(r,e,t);case 4:return w8(r,e,t);case 5:return I8(r,e);case 6:return v8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function p8(r){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${r.texture2D}(textureSampler, uv).r;\n    }\n  `}function c8(r){return`\n    void setOutput(float val) {\n      ${r.output} = vec4(val, 0, 0, 0);\n    }\n  `}function l8(r){return`\n    void setOutput(vec4 val) {\n      ${r.output} = val;\n    }\n  `}function m8(r){return`${r.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${r.varyingFs} vec2 resultUV;\n    ${r.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    ${r.defineSpecialNaN}\n    ${r.defineSpecialInf}\n    ${r.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    ${d8}\n    ${f8}\n    ${h8}\n  `}var d8=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,f8=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,h8=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,g8=`\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;function O$(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function x8(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?`\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 * ${o[1]}.0);\n      }\n    `:o[1]===1?t?`\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 * ${o[0]}.0);\n      }\n    `:t?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${o[0]}, ${o[1]}));\n      return 2 * (resTexRC.x * ${o[1]} + resTexRC.y);\n    }\n  `}function y8(r,e,t){return e[0]===1?t?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${e[1]}.0);\n      }\n    `:e[1]===1?t?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${e[0]}.0);\n      }\n    `:t?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${e[0]}, ${e[1]}));\n      return resTexRC.x * ${e[1]} + resTexRC.y;\n    }\n  `}function b8(r,e,t){if(t)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[2]/2),s=n*Math.ceil(r[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${o[0]}, ${o[1]}));\n      int index = resTexRC.x * ${o[1]} + resTexRC.y;\n\n      int b = index / ${s};\n      index -= b * ${s};\n\n      int r = 2 * (index / ${n});\n      int c = imod(index, ${n}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function C8(r,e,t){if(t)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    ${Pu([\"r\",\"c\",\"d\"],r)}\n    return ivec3(r, c, d);\n  }\n`;let o=ps([\"r\",\"c\",\"d\"],r);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${e[0]}, ${e[1]}));\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n      ${o}\n      return ivec3(r, c, d);\n    }\n  `}function S8(r,e,t){if(t)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[r.length-1]/2),s=n*Math.ceil(r[r.length-2]/2),a=s,i=\"\",p=\"b, r, c\";for(let u=2;u<r.length-1;u++)a*=r[r.length-u-1],i=`\n      int b${u} = index / ${a};\n      index -= b${u} * ${a};\n    `+i,p=`b${u}, `+p;return`\n    ivec${r.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${o[0]}, ${o[1]}));\n      int index = resTexRC.x * ${o[1]} + resTexRC.y;\n\n      ${i}\n\n      int b = index / ${s};\n      index -= b * ${s};\n\n      int r = 2 * (index / ${n});\n      int c = imod(index, ${n}) * 2;\n\n      return ivec${r.length}(${p});\n    }\n  `}function w8(r,e,t){if(t)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      ${Pu([\"r\",\"c\",\"d\",\"d2\"],r)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let o=ps([\"r\",\"c\",\"d\",\"d2\"],r);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${e[0]}, ${e[1]}));\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n      ${o}\n      return ivec4(r, c, d, d2);\n    }\n  `}function I8(r,e){let t=ps([\"r\",\"c\",\"d\",\"d2\",\"d3\"],r);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},\n                             ${e[1]}));\n\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n\n      ${t}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function v8(r,e){let t=ps([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],r);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${e[0]}, ${e[1]}));\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n\n      ${t}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function k8(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return t?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${o[0]}, ${o[1]}));\n      }\n    `;let n=Math.ceil(r[1]/2);return t?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${o[0]}, ${o[1]}));\n\n      int index = resTexRC.x * ${o[1]} + resTexRC.y;\n      int r = 2 * (index / ${n});\n      int c = imod(index, ${n}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function N8(r,e,t){return y.arraysEqual(r,e)?t?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));\n      }\n    `:r[1]===1?t?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${e[0]}, ${e[1]}));\n        int index = resTexRC.x * ${e[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:r[0]===1?t?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${e[0]}, ${e[1]}));\n        int index = resTexRC.x * ${e[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:t?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${e[0]}, ${e[1]}));\n      int index = resTexRC.x * ${e[1]} + resTexRC.y;\n      int r = index / ${r[1]};\n      int c = index - r * ${r[1]};\n      return ivec2(r, c);\n    }\n  `}function Mu(r){return`offset${r}`}function T8(r){let e=r.name,t=\"get\"+e.charAt(0).toUpperCase()+e.slice(1),o=wt();return`\n    vec4 ${t}() {\n      return ${o.texture2D}(${e}, halfCR);\n    }\n  `}function _8(r,e){let t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`float ${o}() {return ${t};}`;let[n,s]=r.shapeInfo.texShape;if(n===1&&s===1)return`\n      float ${o}() {\n        return sampleTexture(${t}, halfCR);\n      }\n    `;let a=Mu(t);if(e)return`\n    float ${o}() {\n      vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], ${a});\n      return sampleTexture(${t}, uv);\n    }\n  `;let[i,p]=r.shapeInfo.texShape;return`\n    float ${o}() {\n      vec2 uv = uvFromFlat(${i}, ${p}, ${a});\n      return sampleTexture(${t}, uv);\n    }\n  `}function $8(r,e){let t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=wt();if(e)return`\n    vec4 ${o}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${s.texture2D}(${t}, uv);\n    }\n  `;let a=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];return`\n    vec4 ${o}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${a[0]}, ${a[1]}, index);\n      return ${s.texture2D}(${t}, uv);\n    }\n  `}function E8(r,e){let t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`\n      float ${o}(int index) {\n        ${pc(r)}\n      }\n    `;let n=r.shapeInfo.texShape,s=n[0],a=n[1];if(a===1&&s===1)return`\n      float ${o}(int index) {\n        return sampleTexture(${t}, halfCR);\n      }\n    `;let i=Mu(t);return a===1?e?`\n      float ${o}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t}TexShape[0]));\n        return sampleTexture(${t}, uv);\n      }\n    `:`\n      float ${o}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `:s===1?e?`\n      float ${o}(int index) {\n        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t}TexShape[1]), 0.5);\n        return sampleTexture(${t}, uv);\n      }\n    `:`\n      float ${o}(int index) {\n        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${a}.0, 0.5);\n        return sampleTexture(${t}, uv);\n      }\n    `:e?`\n    float ${o}(int index) {\n      vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], index + ${i});\n      return sampleTexture(${t}, uv);\n    }\n  `:`\n    float ${o}(int index) {\n      vec2 uv = uvFromFlat(${s}, ${a}, index + ${i});\n      return sampleTexture(${t}, uv);\n    }\n  `}function A8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],p=wt();if(s!=null&&y.arraysEqual(t,s))return e?`\n      vec4 ${n}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);\n\n        return ${p.texture2D}(${o}, uv);\n      }\n    `:`\n      vec4 ${n}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${a}.0);\n\n        return ${p.texture2D}(${o}, uv);\n      }\n    `;if(e)return`\n    vec4 ${n}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${o}TexShape[0]) / 2.0), ceil(float(${o}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${o}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${p.texture2D}(${o}, uv);\n    }\n  `;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(t[1]/2);return`\n    vec4 ${n}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);\n      return ${p.texture2D}(${o}, uv);\n    }\n  `}function R8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(t,s)){if(e)return`\n      float ${n}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `;let m=s[0],d=s[1];return`\n    float ${n}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${m}.0);\n      return sampleTexture(${o}, uv);\n    }\n  `}let{newShape:a,keptDims:i}=y.squeezeShape(t),p=a;if(p.length<t.length){let m=cc(r,p),d=[\"row\",\"col\"];return`\n      ${uc(m,e)}\n      float ${n}(int row, int col) {\n        return ${n}(${lc(d,i)});\n      }\n    `}if(r.shapeInfo.isUniform)return`\n      float ${n}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));\n        ${pc(r)}\n      }\n    `;let u=s[0],c=s[1],l=Mu(o);return c===1?e?`\n      float ${n}(int row, int col) {\n        float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${o}TexShape[0]));\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n    float ${n}(int row, int col) {\n      float index = dot(vec3(row, col, ${l}), vec3(${t[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);\n      return sampleTexture(${o}, uv);\n    }\n  `:u===1?e?`\n      float ${n}(int row, int col) {\n        float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${o}TexShape[1]), 0.5);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n    float ${n}(int row, int col) {\n      float index = dot(vec3(row, col, ${l}), vec3(${t[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${o}, uv);\n    }\n  `:e?`\n      float ${n}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${o}Shape[1] + col + ${l};\n        vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n  float ${n}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${t[1]} + col + ${l};\n    vec2 uv = uvFromFlat(${u}, ${c}, index);\n    return sampleTexture(${o}, uv);\n  }\n`}function D8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(t[0]===1){let m=t.slice(1),d=[1,2],f=cc(r,m),h=[\"b\",\"row\",\"col\"];return`\n        ${F$(f,e)}\n        vec4 ${n}(int b, int row, int col) {\n          return ${n}(${lc(h,d)});\n        }\n      `}let i=wt();if(e)return`\n    vec4 ${n}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${o}TexShape[0]) / 2.0), ceil(float(${o}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${o}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${o}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${i.texture2D}(${o}, uv);\n    }\n  `;let p=a[0],u=a[1],c=Math.ceil(t[2]/2),l=c*Math.ceil(t[1]/2);return`\n    vec4 ${n}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${p}, ${u}, ${l}, ${c}, b, row, col);\n      return ${i.texture2D}(${o}, uv);\n    }\n  `}function F8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=t[1]*t[2],a=t[2],{newShape:i,keptDims:p}=y.squeezeShape(t),u=i;if(u.length<t.length){let h=cc(r,u),g=[\"row\",\"col\",\"depth\"];return`\n        ${uc(h,e)}\n        float ${n}(int row, int col, int depth) {\n          return ${n}(${lc(g,p)});\n        }\n      `}if(r.shapeInfo.isUniform)return`\n      float ${n}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${s}, ${a}, 1)));\n        ${pc(r)}\n      }\n    `;let c=r.shapeInfo.texShape,l=c[0],m=c[1],d=r.shapeInfo.flatOffset;if(m===s&&d==null)return e?`\n      float ${n}(int row, int col, int depth) {\n        int stride1 = ${o}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n        float ${n}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${a}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${m}.0, ${l}.0);\n          return sampleTexture(${o}, uv);\n        }\n      `;if(m===a&&d==null)return e?`\n      float ${n}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${o}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n    float ${n}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${l}.0);\n      return sampleTexture(${o}, uv);\n    }\n  `;let f=Mu(o);return e?`\n    float ${n}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${o}Shape[1] * ${o}Shape[2];\n      int stride1 = ${o}Shape[2];\n      int index = row * stride0 + col * stride1 + depth + ${f};\n      vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index);\n      return sampleTexture(${o}, uv);\n    }\n    `:`\n      float ${n}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s} + col * ${a} + depth + ${f};\n        vec2 uv = uvFromFlat(${l}, ${m}, index);\n        return sampleTexture(${o}, uv);\n      }\n  `}function O8(r,e){let t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),n=wt();if(e)return`\n    vec4 ${o}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${t}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${t}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${t}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}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 ${n.texture2D}(${t}, uv);\n    }\n  `;let s=r.shapeInfo.logicalShape,a=s.length,i=r.shapeInfo.texShape,p=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=p[0],c=p[1],l=Math.ceil(s[a-1]/2),m=l*Math.ceil(s[a-2]/2),d=\"int b, int row, int col\",f=`b * ${m} + (row / 2) * ${l} + (col / 2)`;for(let h=2;h<a-1;h++)d=`int b${h}, `+d,m*=s[a-h-1],f=`b${h} * ${m} + `+f;return`\n    vec4 ${o}(${d}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});\n      return ${n.texture2D}(${t}, uv);\n    }\n  `}function P8(r,e){let t=r.shapeInfo.logicalShape,o=r.name,n=\"get\"+o.charAt(0).toUpperCase()+o.slice(1),s=t[3],a=t[2]*s,i=t[1]*a,{newShape:p,keptDims:u}=y.squeezeShape(t);if(p.length<t.length){let b=cc(r,p),C=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${uc(b,e)}\n      float ${n}(int row, int col, int depth, int depth2) {\n        return ${n}(${lc(C,u)});\n      }\n    `}if(r.shapeInfo.isUniform)return`\n      float ${n}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${i}, ${a}, ${s}, 1)));\n        ${pc(r)}\n      }\n    `;let c=r.shapeInfo.flatOffset,l=r.shapeInfo.texShape,m=l[0],d=l[1],f=`int stride2 = ${o}Shape[3];`,h=`int stride1 = ${o}Shape[2] * stride2;`,g=`int stride0 = ${o}Shape[1] * stride1;`;if(d===i&&c==null)return e?`\n      float ${n}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${h}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n      float ${n}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${a}, ${s}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${d}.0, ${m}.0);\n        return sampleTexture(${o}, uv);\n      }\n    `;if(d===s&&c==null)return e?`\n      float ${n}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${o}Shape[1] * ${o}Shape[2], ${o}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${o}TexShape[1], ${o}TexShape[0]);\n        return sampleTexture(${o}, uv);\n      }\n    `:`\n      float ${n}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${t[1]*t[2]}, ${t[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${d}.0, ${m}.0);\n        return sampleTexture(${o}, uv);\n      }\n    `;let x=Mu(o);return e?`\n    float ${n}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${h}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index + ${x});\n      return sampleTexture(${o}, uv);\n    }\n  `:`\n    float ${n}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${i} + col * ${a} +\n          depth * ${s} + depth2;\n      vec2 uv = uvFromFlat(${m}, ${d}, index + ${x});\n      return sampleTexture(${o}, uv);\n    }\n  `}function M8(r){let e=r.shapeInfo.logicalShape,t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:p,keptDims:u}=y.squeezeShape(e);if(p.length<e.length){let h=cc(r,p),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${uc(h)}\n      float ${o}(int row, int col, int depth, int depth2, int depth3) {\n        return ${o}(${lc(g,u)});\n      }\n    `}if(r.shapeInfo.isUniform)return`\n      float ${o}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${i}, ${a}, ${s}, ${n})) +\n          depth3;\n        ${pc(r)}\n      }\n    `;let c=r.shapeInfo.flatOffset,l=r.shapeInfo.texShape,m=l[0],d=l[1];if(d===i&&c==null)return`\n      float ${o}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${a}, ${s}, ${n}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${d}.0, ${m}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `;if(d===n&&c==null)return`\n      float ${o}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${e[1]*e[2]*e[3]},\n               ${e[2]*e[3]}, ${e[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${d}.0, ${m}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `;let f=Mu(t);return`\n    float ${o}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${i} + col * ${a} + depth * ${s} +\n          depth2 * ${n} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${m}, ${d}, index);\n      return sampleTexture(${t}, uv);\n    }\n  `}function L8(r){let e=r.shapeInfo.logicalShape,t=r.name,o=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=y.squeezeShape(e);if(n.length<e.length){let g=cc(r,n),x=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${uc(g)}\n      float ${o}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${o}(${lc(x,s)});\n      }\n    `}let a=e[5],i=e[4]*a,p=e[3]*i,u=e[2]*p,c=e[1]*u;if(r.shapeInfo.isUniform)return`\n      float ${o}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${u}, ${p}, ${i})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${a}, 1)));\n        ${pc(r)}\n      }\n    `;let l=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,d=m[0],f=m[1];if(f===c&&l==null)return`\n      float ${o}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${u}, ${p}, ${i}, ${a})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${d}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `;if(f===a&&l==null)return`\n      float ${o}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${e[1]*e[2]*e[3]*e[4]},\n               ${e[2]*e[3]*e[4]},\n               ${e[3]*e[4]},\n               ${e[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${d}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `;let h=Mu(t);return`\n    float ${o}(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 * ${u} + depth * ${p} +\n          depth2 * ${i} + depth3 * ${a} + depth4 + ${h};\n      vec2 uv = uvFromFlat(${d}, ${f}, index);\n      return sampleTexture(${t}, uv);\n    }\n  `}function pc(r){let e=r.name,t=y.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`\n    for (int i = 0; i < ${t}; i++) {\n      if (i == index) {\n        return ${e}[i];\n      }\n    }\n  `}function B8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n=\"get\"+o+\"AtOutCoords\",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=R$(r.shapeInfo.logicalShape,e.logicalShape),p=$e(a),u=a-s,c,l=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];s===0?c=\"\":a<2&&i.length>=1?c=\"coords = 0;\":c=i.map(b=>`coords.${l[b+u]} = 0;`).join(`\n`);let m=\"\";a<2&&s>0?m=\"coords\":m=r.shapeInfo.logicalShape.map((b,C)=>`coords.${l[C+u]}`).join(\", \");let d=\"return outputValue;\",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)d=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(h&&!x)a===1?d=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:d=`\n        return vec4(outputValue.x);\n      `;else if(i.length){let b=s-2,C=s-1;i.indexOf(b)>-1&&i.indexOf(C)>-1?d=\"return vec4(outputValue.x);\":i.indexOf(b)>-1?d=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":i.indexOf(C)>-1&&(d=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n    vec4 ${n}() {\n      ${p} coords = getOutputCoords();\n      ${c}\n      vec4 outputValue = get${o}(${m});\n      ${d}\n    }\n  `}function V8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n=\"get\"+o+\"AtOutCoords\",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,p=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===p&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`\n      float ${n}() {\n        return sampleTexture(${t}, resultUV);\n      }\n    `;let u=$e(p),c=R$(r.shapeInfo.logicalShape,e.logicalShape),l=p-i,m,d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];i===0?m=\"\":p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(h=>`coords.${d[h+l]} = 0;`).join(`\n`);let f=\"\";return p<2&&i>0?f=\"coords\":f=r.shapeInfo.logicalShape.map((h,g)=>`coords.${d[g+l]}`).join(\", \"),`\n    float ${n}() {\n      ${u} coords = getOutputCoords();\n      ${m}\n      return get${o}(${f});\n    }\n  `}function $e(r){if(r<=1)return\"int\";if(r===2)return\"ivec2\";if(r===3)return\"ivec3\";if(r===4)return\"ivec4\";if(r===5)return\"ivec5\";if(r===6)return\"ivec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Of(r,e,t){let{newShape:o,keptDims:n}=y.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):o,p=!r&&s>1&&!y.arraysEqual(e,t)&&o.length<s||a;return{useSqueezeShape:p,uniformShape:p?i:e,keptDims:n}}function cc(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function lc(r,e){return e.map(t=>r[t]).join(\", \")}function M$(r,e,t,o){let n=t.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:e.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=D$(n,a,e),p=jS(r.gl,i),u=r.createProgram(p);return O().get(\"ENGINE_COMPILE_ONLY\")?{program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},mw(r,e,u))}function mw(r,e,t){let o={},n={},s={},a=[],i,p,u,c=null,l=null;l=r.getUniformLocation(t,\"NAN\",!1),O().getNumber(\"WEBGL_VERSION\")===1&&(c=r.getUniformLocation(t,\"INFINITY\",!1));let m=!1;for(let d=0;d<e.variableNames.length;d++){let f=e.variableNames[d];o[f]=r.getUniformLocation(t,f,m),o[`offset${f}`]=r.getUniformLocation(t,`offset${f}`,m),e.enableShapeUniforms&&(n[`${f}Shape`]=r.getUniformLocation(t,`${f}Shape`,m),s[`${f}TexShape`]=r.getUniformLocation(t,`${f}TexShape`,m))}return e.enableShapeUniforms&&(i=r.getUniformLocation(t,\"outShape\",m),u=r.getUniformLocation(t,\"outShapeStrides\",m),p=r.getUniformLocation(t,\"outTexShape\",m)),e.customUniforms&&e.customUniforms.forEach((d,f)=>{a[f]=r.getUniformLocation(t,d.name,m)}),{uniformLocations:o,customUniformLocations:a,infLoc:c,nanLoc:l,inShapesLocations:n,inTexShapesLocations:s,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:p}}function P$(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,o)=>{let n=t.logicalShape,s=e[o],a=s.shape;if(!y.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,p=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(i,p))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${p} must match`)})}function L$(r,e,t,o,n){e.program.enableShapeUniforms||(P$(e.inShapeInfos,t),P$([e.outShapeInfo],[o]));let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,a[0],a[1]):r.setOutputMatrixTexture(s.texture,a[0],a[1]),r.setProgram(e.webGLProgram),O().getNumber(\"WEBGL_VERSION\")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((p,u)=>{let c=e.program.variableNames[u],l=e.uniformLocations[c],m=e.uniformLocations[`offset${c}`],d=e.inShapesLocations[`${c}Shape`],f=e.inTexShapesLocations[`${c}TexShape`];if(d){let{uniformShape:h}=Of(e.program.packedInputs,p.shape,p.texData.texShape);switch(h.length){case 1:r.gl.uniform1iv(d,new Int32Array(h));break;case 2:r.gl.uniform2iv(d,new Int32Array(h));break;case 3:r.gl.uniform3iv(d,new Int32Array(h));break;case 4:r.gl.uniform4iv(d,new Int32Array(h));break;default:break}}if(f&&r.gl.uniform2i(f,p.texData.texShape[0],p.texData.texShape[1]),l!=null){if(p.isUniform){if(y.sizeFromShape(p.shape)<2)r.gl.uniform1f(l,p.uniformValues[0]);else{let h=p.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),r.gl.uniform1fv(l,h)}return}p.texData.slice!=null&&m!=null&&r.gl.uniform1i(m,p.texData.slice.flatOffset),r.setInputMatrixTexture(p.texData.texture.texture,l,u)}});let i=e.outShapeLocation;if(i)switch(o.shape.length){case 1:r.gl.uniform1iv(i,new Int32Array(o.shape));break;case 2:r.gl.uniform2iv(i,new Int32Array(o.shape));break;case 3:r.gl.uniform3iv(i,new Int32Array(o.shape));break;case 4:r.gl.uniform4iv(i,new Int32Array(o.shape));break;default:break}if(e.outShapeStridesLocation){let p=y.computeStrides(o.shape);switch(o.shape.length){case 2:r.gl.uniform1iv(e.outShapeStridesLocation,new Int32Array(p));break;case 3:r.gl.uniform2iv(e.outShapeStridesLocation,new Int32Array(p));break;case 4:r.gl.uniform3iv(e.outShapeStridesLocation,new Int32Array(p));break;default:break}}e.outTexShapeLocation&&r.gl.uniform2i(e.outTexShapeLocation,o.texData.texShape[0],o.texData.texShape[1]),e.program.customUniforms&&n&&e.program.customUniforms.forEach((p,u)=>{let c=e.customUniformLocations[u],l=n[u];if(p.type===\"float\")r.gl.uniform1fv(c,l);else if(p.type===\"vec2\")r.gl.uniform2fv(c,l);else if(p.type===\"vec3\")r.gl.uniform3fv(c,l);else if(p.type===\"vec4\")r.gl.uniform4fv(c,l);else if(p.type===\"int\")r.gl.uniform1iv(c,l);else if(p.type===\"ivec2\")r.gl.uniform2iv(c,l);else if(p.type===\"ivec3\")r.gl.uniform3iv(c,l);else if(p.type===\"ivec4\")r.gl.uniform4iv(c,l);else throw Error(`uniform type ${p.type} is not supported yet.`)}),r.executeProgram()}function B$(r,e,t){let o=\"\";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Of(r.packedInputs,a.shape,p),m=\"\",d=\"\",f=\"\";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=S.getBroadcastDims(a.shape,t.shape),C=!r.packedInputs&&h===t.shape.length&&y.arraysEqual(p,t.texData.texShape),w=r.packedInputs||c.length>2?\"\":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${w}_${i}`}else{let p=a.isUniform?\"uniform\":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+=\"_\"+o+\"_\"+n+`${O().getNumber(\"WEBGL_VERSION\")}`,s}function lt(r){return O().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Pf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Li.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Pu([\"r\",\"c\",\"d\"],e):ps([\"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    `}};var Mf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Li.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Pu([\"r\",\"c\",\"d\"],e):ps([\"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    `}};var Lf=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=ur.DOWNLOAD;let t=wt();this.outputShape=e,this.userCode=`\n      ${Ff}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}};var Bf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ur.DOWNLOAD;let t=wt();this.outputShape=e,this.userCode=`\n      ${Ff}\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    `}};var U8={R:0,G:1,B:2,A:3},Tl=class{constructor(e,t=!1,o=\"RGBA\"){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\");let a=\"\";for(let i=0;i<o.length;i++){let p=o[i];a+=`\n          if(offset == ${i}) {\n            result = values[${U8[p]}];\n          }`}this.userCode=`\n      ${this.enableShapeUniforms?ac():sc(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int flatIndex = getFlatIndex(coords);\n        float result = 0.;\n        int offset = imod(flatIndex, ${o.length});\n\n        flatIndex = idiv(flatIndex, ${o.length}, 1.);\n\n        int r = flatIndex / texShape[1];\n        if (r < texShape[0]) {\n          int c = imod(flatIndex, texShape[1]);\n          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n          vec4 values = ${n.texture2D}(A, uv);\n          ${a}\n        }\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}};var Vf=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let o=wt();this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let n=\"\",s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let p=a*2+i;n+=`\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 = ${o.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${p}] = values[0];\n            } else if (offset == 1) {\n              result[${p}] = values[1];\n            } else if (offset == 2) {\n              result[${p}] = values[2];\n            } else {\n              result[${p}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?ac():sc(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          ${n}\n\n          ${o.output} = ${s};\n        }\n    `}};var $w={};Ge($w,{bindVertexProgramAttributeStreams:()=>Sw,createBufferFromOutputTexture:()=>vw,createFloat16MatrixTexture:()=>xw,createFloat16PackedMatrixTexture:()=>Cw,createFloat32MatrixTexture:()=>gw,createIndexBuffer:()=>hw,createPackedMatrixTexture:()=>bw,createUnsignedBytesMatrixTexture:()=>yw,createVertexBuffer:()=>fw,createVertexShader:()=>dw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Nw,downloadFloat32MatrixFromBuffer:()=>kw,downloadMatrixFromPackedOutputTexture:()=>_w,downloadPackedMatrixFromBuffer:()=>Tw,getInternalFormatForFloat16MatrixTexture:()=>Wf,getInternalFormatForFloat16PackedMatrixTexture:()=>Hf,getInternalFormatForFloat32MatrixTexture:()=>zf,getInternalFormatForPackedMatrixTexture:()=>Gf,getInternalFormatForUnsignedBytesMatrixTexture:()=>Uf,uploadDenseMatrixToTexture:()=>ww,uploadPixelDataToTexture:()=>Iw});function dw(r){let e=wt(),t=`${e.version}\n    precision highp float;\n    ${e.attribute} vec3 clipSpacePos;\n    ${e.attribute} vec2 uv;\n    ${e.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;return qS(r,t)}function fw(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return QS(r,e)}function hw(r){let e=new Uint16Array([0,1,2,2,1,3]);return ZS(r,e)}function _l(r,e,t,o,n,s){ew(e,t);let a=JS(r),i=r.TEXTURE_2D;return pe(r,()=>r.bindTexture(i,a)),pe(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),pe(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),pe(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),pe(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),O().getNumber(\"WEBGL_VERSION\")===1?pe(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)):pe(r,()=>r.texStorage2D(i,1,o,e,t)),pe(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:a,texShape:[t,e]}}function zf(r){return r.internalFormatFloat}function gw(r,e,t,o){let[n,s]=Ou(e,t);return _l(r,n,s,zf(o),o.textureFormatFloat,r.FLOAT)}function Wf(r){return r.internalFormatHalfFloat}function xw(r,e,t,o){let[n,s]=Ou(e,t);return _l(r,n,s,Wf(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function Uf(r){return r.downloadTextureFormat}function yw(r,e,t,o){let[n,s]=Ou(e,t);return _l(r,n,s,Uf(o),r.RGBA,r.UNSIGNED_BYTE)}function Gf(r){return r.internalFormatPackedFloat}function bw(r,e,t,o){let[n,s]=Qs(e,t);return _l(r,n,s,Gf(o),r.RGBA,r.FLOAT)}function Hf(r){return r.internalFormatPackedHalfFloat}function Cw(r,e,t,o){let[n,s]=Qs(e,t);return _l(r,n,s,Hf(o),r.RGBA,o.textureTypeHalfFloat)}function Sw(r,e,t){return pe(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Af(r,e,\"clipSpacePos\",t,3,20,0)&&Af(r,e,\"uv\",t,2,20,12)}function ww(r,e,t,o,n,s){pe(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,p;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,p=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,p=s.internalFormatPackedFloat),a.set(n),O().getNumber(\"WEBGL_VERSION\")===2?pe(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t,o,r.RGBA,i,a)):pe(r,()=>r.texImage2D(r.TEXTURE_2D,0,p,t,o,0,r.RGBA,i,a)),pe(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Iw(r,e,t){pe(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?O().getNumber(\"WEBGL_VERSION\")===2?pe(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t.width,t.height,r.RGBA,r.UNSIGNED_BYTE,t.data)):pe(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):O().getNumber(\"WEBGL_VERSION\")===2?pe(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,t)):pe(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),pe(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function vw(r,e,t,o){let n=r.createBuffer();pe(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return pe(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),pe(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),pe(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function kw(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function Nw(r,e,t,o){let[n,s]=Ou(e,t),a=4,i=new Uint8Array(k$(e*t,a));return pe(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function Tw(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(N$(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function _w(r,e,t){let o=new Float32Array(e*t*4);return pe(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var Lu=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=O().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,US(t,e)):this.gl=zr(t),e=this.gl,O().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>pe(s,()=>s.createVertexArray()),this.bindVertexArray=a=>pe(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>pe(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>pe(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>pe(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>pe(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>pe(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>pe(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),O().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=tc(this.gl,s),Wr(this.gl,a))this.textureHalfFloatExtension=tc(this.gl,a);else if(O().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(o),Wr(this.gl,n))this.colorBufferHalfFloatExtension=tc(this.gl,n);else if(O().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(o=\"EXT_color_buffer_float\",Wr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Wr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=fw(this.gl),this.indexBuffer=hw(this.gl),this.framebuffer=tw(this.gl),this.textureConfig=vl(this.gl,this.textureHalfFloatExtension)}get debug(){return O().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;pe(e,()=>e.finish()),pe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),pe(e,()=>e.deleteFramebuffer(this.framebuffer)),pe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),pe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),pe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),gw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),xw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),yw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Iw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),ww(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Cw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),bw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Rf(this.gl,this.framebuffer),this.outputTexture=null),pe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>Nw(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return Tw(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return kw(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=vw(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(O().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>_w(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=dw(t));let o=XS(t);pe(t,()=>t.attachShader(o,this.vertexShader)),pe(t,()=>t.attachShader(o,e)),YS(t,o);let n;return n=Object.assign(o,{vao:this.createVertexArray()}),this.bindVertexArray(n.vao),pe(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(Sw(t,n,this.vertexBuffer),\"gpgpu_util.bindVertexProgramAttributeStreams not fully successful.\"),this.debug&&kl(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(pe(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&kl(this.gl,this.program)),pe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?rw(this.gl,e,t):ow(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),pe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),nw(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Qs(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&kl(this.gl,this.program),rc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}pe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),pe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=tc(this.gl,O().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(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=G8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in O().platform&&(o=O().platform.setTimeoutCustom.bind(O().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Nl(this.gl,e,this.framebuffer),this.debug&&rc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Nl(this.gl,this.outputTexture,this.framebuffer),this.debug&&rc(this.gl)):Rf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Nl(n,e,this.framebuffer),this.debug&&rc(n),this.outputTexture=e,pe(n,()=>n.viewport(0,0,t,o)),pe(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),pe(this.gl,()=>this.gl.scissor(e,t,o,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 G8(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{addImpl:V$,bincountImpl:Kf,bincountReduceImpl:z$,castImpl:W$,ceilImpl:U$,concatImpl:G$,equalImpl:H$,expImpl:K$,expm1Impl:q$,floorImpl:j$,gatherNdImpl:X$,gatherV2Impl:Y$,greaterImpl:Q$,greaterEqualImpl:Z$,lessImpl:J$,lessEqualImpl:eE,linSpaceImpl:tE,logImpl:rE,maxImpl:oE,maximumImpl:nE,minimumImpl:sE,multiplyImpl:aE,negImpl:iE,notEqualImpl:uE,prodImpl:pE,raggedGatherImpl:cE,raggedRangeImpl:lE,raggedTensorToTensorImpl:mE,rangeImpl:dE,rsqrtImpl:fE,scatterImpl:hE,sigmoidImpl:gE,simpleAbsImpl:qf,sliceImpl:xE,sparseFillEmptyRowsImpl:yE,sparseReshapeImpl:bE,sparseSegmentReductionImpl:jf,sqrtImpl:CE,stridedSliceImpl:SE,stringNGramsImpl:wE,stringSplitImpl:IE,stringToHashBucketFastImpl:vE,subImpl:kE,tileImpl:NE,topKImpl:TE,transposeImpl:Bu,uniqueImpl:_E}=Zp;function Ew(r,e){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,e).map(t=>`${r}.${t}`)}function Et(r,e){return e===1?[r]:Ew(r,e)}function $E(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o<r;o++)t+=e[o],o<r-1&&(t+=\",\");return t}var Xf=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=lt(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=Et(\"rc\",this.rank),o=$e(this.rank),n=this.getOutOfBoundsCondition(t),s=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${o} rc = getOutputCoords();\n\n          if(${n}) {\n            setOutput(vec4(0));\n          } else {\n            ${s}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let o=0;o<=1;o++)for(let n=0;n<=1;n++){let s=`${o===0?\"r\":\"rp1\"}, ${n===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o<this.rank;o++)t+=`${e[o]} >= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),o=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],n=this.enableShapeUniforms?`outShape[${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 >= ${o};\n      bool rEdge = rp1 >= ${n};\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]})`}};var mc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n        ${s}\n        ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${n}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${n>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${H8(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?ac():sc(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        ${o}\n\n        setOutput(result);\n      }\n    `}};function H8(r,e){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${e?A$([\"r\",\"c\",\"d\"],\"inputShape\"):ps([\"r\",\"c\",\"d\"],r)}\n      return ivec3(r, c, d);\n    }\n  `}var Yf=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,o){let n=AE(t,o),s=RE(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=EE(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let i;return n===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Zt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=AE(o,n),a=RE(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=EE(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=O().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(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],c=u.indexOf(e);if(c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(c,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 K8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function EE(r,e,t,o,n){let s=q8(e,o),a;if(n){let[p,u]=Qs(r[0],r[1]);a=p*u}else{let[p,u]=Ou(r[0],r[1]);a=p*u}let i=K8(t,s);return a*i}function q8(r,e){switch(r){case Zt.PACKED_2X2_FLOAT32:return Gf(e);case Zt.PACKED_2X2_FLOAT16:return Hf(e);case Zt.UNPACKED_FLOAT32:return zf(e);case Zt.UNPACKED_FLOAT16:return Wf(e);case Zt.PACKED_4X1_UNSIGNED_BYTE:return Uf(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function j8(r){return O().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:r?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function AE(r,e){if(r===ur.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(r===ur.RENDER||r==null)return j8(e);if(r===ur.DOWNLOAD||r===ur.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function RE(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Jt=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=lt(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    `}},zt=\"if (isnan(x)) return x;\",DE=\"return x;\",Aw=\"return abs(x);\";var FE=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",OE=zt+`\n  return (x < 0.0) ? 0.0 : x;\n`,PE=zt+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Zs=\"return x;\",ME=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var BE=\"return x;\",VE=`\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`,zE=`\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`,WE=`\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`,UE=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Er=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}};var Qf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let t=e.length,o=Et(\"rc\",t),n=$e(t),s=$E(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${n} rc = getOutputCoords();\n        vec4 packedInput = getA(${s});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}};var Y8=Vt.whereImpl,Q8=1e-7,Z8=1e-4,Zf={};function J8(r){return r in Zf||(Zf[r]={}),Zf[r]}var eY=O().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),tY=600;function rY(){return O().global.screen==null?1024:O().global.screen.height*O().global.screen.width*window.devicePixelRatio*tY/1024/1024}var Vi=class extends Zr{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!O().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Lu)t=e;else{let o=zr(O().getNumber(\"WEBGL_VERSION\"),e);t=new Lu(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=zr(O().getNumber(\"WEBGL_VERSION\"));t=new Lu(o),this.binaryCache=J8(O().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Yf(this.gpgpu),this.numMBBeforeWarning=rY(),this.texData=new Fo(this,sr())}nextDataId(){return Vi.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=oc(t),c=new Tl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((O().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||O().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"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:o,values:e,usage:ur.UPLOAD,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,o,n,s){if(O().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:o,dtype:n,values:t,usage:ur.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Er(i,Zs):m=new Jt(i,Zs);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=S.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Er(n,Zs):f=new Jt(n,Zs);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(O().getBool(\"DEBUG\")&&!O().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&O().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,c;if(a!==\"complex64\"&&O().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Il(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=S.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;pe(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&sr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Er(s,Zs):d=new Jt(s,Zs);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==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 c=this.decode(e,t.customTexShape),l=sr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let o=e[t];if(!KS(o))throw O().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${o} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:o,isPacked:n}=this.texData.get(e),s=y.sizeFromShape(t);if(O().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let m=this.decode(e),d=this.texData.get(m.dataId),f=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Il(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),f}let a=O().getBool(\"WEBGL_PACK\")&&n===!0,i=a?oc(t):t,p=a?new Bf(i):new Lf(i),u=this.runWebGLProgram(p,[{shape:i,dtype:o,dataId:e}],\"float32\"),c=this.texData.get(u.dataId),l=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),l}timerAvailable(){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${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 O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(O().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:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=eY){return O().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return Y8(e.shape,t)}packedUnaryOp(e,t,o){let n=new Er(e.shape,t),s=this.compileAndRun(n,[e],o);return sr().makeTensorFromTensorInfo(s)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let n=qf(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,Aw,e.dtype);let t=new Jt(e.shape,Aw),o=this.compileAndRun(t,[e]);return sr().makeTensorFromTensorInfo(o)}makeTensorInfo(e,t,o){let n;if(t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return sr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new Qf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xf(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Ua(e.shape),...Ga(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Ua(t),...Ga(t)],a=new mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=oc(s),p;n?p=new Mf(i):p=new Pf(i);let u=!0,c=[t!=null?t:Il(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Li.DENSE){let x=a!=null?a:Il(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.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(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=O().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Bi(b.shape,x.shape)){let C=x,w=x.shape;x.shape=b.shape,x=this.packedReshape(x,w),u.push(x),b=this.texData.get(x.dataId),C.shape=w}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=B$(e,c,l),d=this.getAndSaveBinary(m,()=>M$(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),O().get(\"ENGINE_COMPILE_ONLY\")||L$(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=O().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!O().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(O().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=Ee(()=>{if(!O().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=O().getBool(\"DEBUG\");O().set(\"DEBUG\",!1);let t=this.abs(Ce(1e-8)).dataSync()[0];if(O().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Q8:Z8}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=sw(o,p),t.texShape=l),s!=null){let m=oc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Qs(l[0],l[1])),p?d=new Vf(m,g):d=new Tl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=ur.PIXELS:C.usage=ur.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let w=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,w,k),E=this.texData.get(_.dataId);t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,O().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=E.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=oY(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await AC(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Ef(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:p,outShapeStridesLocation:u,outTexShapeLocation:c}=mw(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=p,e.outShapeStridesLocation=u,e.outTexShapeLocation=c}}createTensorFromGPUData(e,t,o){e.channels=e.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=e,p=sr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return sr().makeTensorFromDataId(u,t,o,p)}};Vi.nextDataId=0;function oY(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;o<t.length;++o)t[o]=Math.round(r[o]);return t}else throw new Error(`Unknown dtype ${e}`)}var nY=\"4.2.0\";function GE(){O().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}ki.isBrowser()&&Ti(\"webgl\",()=>new Vi,2);var CJe={forceHalfFloat:GE};var dc=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;var io=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=lt(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    `}};var Js=`\n  result.r = isNaN.r ? NAN : result.r;\n  result.g = isNaN.g ? NAN : result.g;\n  result.b = isNaN.b ? NAN : result.b;\n  result.a = isNaN.a ? NAN : result.a;\n`;var _o=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=lt(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${$e(s)} coords = getOutputCoords();\n        `,s===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) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let p=Et(\"coords\",s);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${p[s-2]} + 1) >= outShape[${s} - 2];\n            bool nextColOutOfBounds =\n              (${p[s-1]} + 1) >= outShape[${s} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n            bool nextColOutOfBounds =\n              (${p[s-1]} + 1) >= ${this.outputShape[s-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 At(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var HE={kernelName:mo,backendName:\"webgl\",kernelFunc:At};function Ar(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var KE={kernelName:ri,backendName:\"webgl\",kernelFunc:Ar};var Rw=\"return (a < 0.) ? b * a : a;\",Dw=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function sY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new _o(Dw,n.shape,a.shape):new io(Rw,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var qE={kernelName:ln,backendName:\"webgl\",kernelFunc:sY};var Fw=\"return (a < 0.) ? b * a : a;\",Ow=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function aY(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new _o(Ow,o.shape,n.shape):new io(Fw,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var jE={kernelName:Rn,backendName:\"webgl\",kernelFunc:aY};var $o=\"if (isnan(x)) return x;\";function he({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Er(a.shape,e):c=new Jt(a.shape,r),i.runWebGLProgram(c,[a],p)}}function tt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[w,k]=C,_={dataId:w.dataId,dtype:w.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},A=new io(r,p.shape,u.shape);return c.runWebGLProgram(A,[_,E],dt(w.dtype,k.dtype))}),b=Ar({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?S.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),w=c.makeTensorInfo(C,l),k=c.texData.get(w.dataId);return k.values=b,w}let m=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new _o(e,p.shape,u.shape,t):d=new io(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Ha(r,e=!1){if(r===\"linear\")return e?BE:DE;if(r===\"relu\")return e?zE:OE;if(r===\"elu\")return e?VE:FE;if(r===\"relu6\")return e?WE:PE;if(r===\"prelu\")return e?Ow:Fw;if(r===\"leakyrelu\")return e?Dw:Rw;if(r===\"sigmoid\")return e?UE:ME;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var fc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=lt(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:u?g=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:g=`vec4 activation(vec4 x) {\n          ${i}\n        }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",w=\"rc.x\";e[0]<t[0]?C=`imod(rc.x, ${e[0]})`:t[0]<e[0]&&(w=`imod(rc.x, ${t[0]})`),this.userCode=`\n      ${g}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${l}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        int batchA = ${C};\n        int batchB = ${w};\n        for (int i = 0; i < ${l}; i++) {\n          vec4 a = getMatrixA(batchA, ${m});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${f[0]} * ${h[0]});\n          result += (${f[1]} * ${h[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${x}\n\n        setOutput(result);\n      }\n    `}};var Pw={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},$l=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),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    `}};var XE=\"return a * b;\";function El(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=S.upcastType(o.dtype,n.dtype);if(o.dtype===\"complex64\"){let i=t.texData.get(o.dataId),p=t.texData.get(n.dataId),u=new $l(Pw.REAL,o.shape,n.shape),c=new $l(Pw.IMAG,o.shape,n.shape),l=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:n.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Ar({inputs:{real:m,imag:d},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),p=t.texData.get(n.dataId),[u,c]=aE(o.shape,n.shape,i.values,p.values,s),l=t.makeTensorInfo(c,s),m=t.texData.get(l.dataId);return m.values=u,l}let a;return O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?a=new _o(XE,o.shape,n.shape):a=new io(XE,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var YE={kernelName:kn,backendName:\"webgl\",kernelFunc:El};function QE(r,e,t){let o=[Ua(r.shape),...Ga(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Ua(e),...Ga(e)],a=new mc(s,o),i=!0,p=[o],u=t.runWebGLProgram(a,[n],r.dtype,p,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}function re(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),p=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(p);y.assert(i===u,()=>`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Bi(n.shape,p)&&!(c.texture!==null&&Bi(c.shape,p))?QE(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ZE={kernelName:_s,backendName:\"webgl\",kernelFunc:re};var Al=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n        if (inIdx < 0 || inIdx >= ${s}) {\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        ${c}\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 * ${o};\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 + ${i};\n        if (${p===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${p===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${p===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}};var Jf=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${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 c=Math.floor(o/4)*4,l=o%4,m=`\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 = ${p}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${p}(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\",m=`\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\",m=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n        if (inIdx < 0 || inIdx >= ${s}) {\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        ${f}\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 * ${o};\n\n        vec4 minMaxValue = vec4(${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 < ${c}; 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          ${m}\n        }\n\n        int inIdx = inOffset + ${c};\n        if (${l===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${m}\n        } else if (${l===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${m}\n        } else if (${l===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${m}\n        }\n        setOutput(${u});\n      }\n    `}};function uY(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Ur(r,e,t,o){let n=uY(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:p,outSize:u}=n[a],c,l;t===\"mean\"?c=a===0?new Al({windowSize:p,inSize:i,batchSize:r.shape[0],outSize:u},i):new Al({windowSize:p,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Jf({windowSize:p,inSize:i,batchSize:r.shape[0],outSize:u},t),l=s,s=o.runWebGLProgram(c,[s],e),l.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(l)}return s}var eh=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=$e(this.rank),s=pY(t);this.userCode=`\n    void main() {\n      ${n} resRC = getOutputCoords();\n      setOutput(getA(${s}));\n    }\n    `}};function pY(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var th=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=$e(this.rank),s=Ew(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,p=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${n} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${p}) {\n        result[1] = ${u};\n      }\n      --${s[this.rank-1]};\n      if(++${s[this.rank-2]} < ${o[this.rank-2]}) {\n        result[2] = ${u};\n        if(${p}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function zi(r,e,t){let o=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new th(r.shape,e):new eh(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function JE(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,p=S.getAxesPermutation(i,s),u=p!=null,c=r;u&&(c=zi(r,p,o),i=S.getInnerMostAxes(i.length,s)),S.assertAxesAreInnerMostDims(\"sum\",i,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,i),d=l;t&&(d=S.expandShapeToKeepDim(l,a));let f=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/f,x=re({inputs:{x:c},attrs:{shape:[g,f]},backend:o}),b=Ta(r.dtype),C=Ur(x,b,\"sum\",o),w=re({inputs:{x:C},attrs:{shape:d},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(C),u&&o.disposeIntermediateTensorInfo(c),w}function Vu(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return JE(n,s,a,t)}var eA={kernelName:Kn,backendName:\"webgl\",kernelFunc:Vu};function yt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,p=new Array(i);for(let c=0;c<p.length;c++)p[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let l=a.texData.get(n.dataId).values,m=Bu(l,n.shape,n.dtype,s,p);u=a.makeTensorInfo(p,n.dtype);let d=a.texData.get(u.dataId);d.values=m}else u=zi(n,s,a);return u}var tA={kernelName:ro,backendName:\"webgl\",kernelFunc:yt};var Mw=1e3;function zu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=yr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=re({inputs:{x:r},backend:n,attrs:{shape:k}}),A=re({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[E,A],D=Math.max(x,b),P=t?E.shape[1]:E.shape[2],M=s!=null,L=a!=null,V=p===\"leakyrelu\",z=p!=null?Ha(p,!0):null,U=M||L||V||z!=null,K;if((d===1||f===1)&&P>Mw&&U===!1){let q=E,Z=A;t&&(q=yt({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),R.push(q)),o&&(Z=yt({inputs:{x:A},backend:n,attrs:{perm:[0,2,1]}}),R.push(Z));let ee=f!==1,oe=f===1,J=q;ee&&(J=re({inputs:{x:q},backend:n,attrs:{shape:[D,P,1]}}),R.push(J));let te=f===1?2:1,ie=Z;oe&&(ie=re({inputs:{x:Z},backend:n,attrs:{shape:[D,1,P]}}),R.push(ie));let ce=El({inputs:{a:J,b:ie},backend:n});K=Vu({inputs:{x:ce},backend:n,attrs:{axis:te,keepDims:!0}}),R.push(ce)}else{let q=dt(r.dtype,e.dtype),Z=new fc(k,_,[D,d,f],t,o,M,z,L,V),ee=[E,A];if(s!=null&&ee.push(s),L&&ee.push(a),V){let oe=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));ee.push(oe),R.push(oe)}K=n.runWebGLProgram(Z,ee,q)}let H=re({inputs:{x:K},backend:n,attrs:{shape:w}});R.push(K);for(let q of R)n.disposeIntermediateTensorInfo(q);return H}function cY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return 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nh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});if(o.length>O().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=nh({inputs:o.slice(0,p),backend:t}),c=nh({inputs:o.slice(p),backend:t});return nh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=O().getBool(\"WEBGL_PACK\")?new oh(o[0].shape,s):new rh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Po,backendName:\"webgl\",kernelFunc:nh};function xY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=yt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=re({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Ur(h,h.dtype,\"all\",t),x;if(a){let 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t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var lA={kernelName:Lo,backendName:\"webgl\",kernelFunc:yY};var sh=class{constructor(e,t,o){this.variableNames=[\"A\"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,a];let i=t===\"max\"?\">\":\"<\",p=o?\"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 * ${n};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${n}; i++) {\n          int inIdx = ${p};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}};var ah=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=$e(p),c=Et(\"coords\",p),l,m;if(a===1){m=p+1;let A=$e(m);l=`\n        ${A} sourceLocR = ${A}(${c.join()}, 0);\n        ++${c[p-1]};\n        ${A} sourceLocG = ${A}(${c.join()}, 0);\n        ++${c[p-2]};\n        ${A} sourceLocA = ${A}(${c.join()}, 0);\n        --${c[p-1]};\n        ${A} sourceLocB = ${A}(${c.join()}, 0);\n        --${c[p-2]};`}else m=p,l=`\n        ${u} sourceLocR = coords;\n        ++${c[p-1]};\n        ${u} sourceLocG = coords;\n        ++${c[p-2]};\n        ${u} sourceLocA = coords;\n        --${c[p-1]};\n        ${u} sourceLocB = coords;\n        --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(A=>\"int \"+A),g=Et(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=Et(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=Et(\"sourceLocB\",m-1).concat(\"inIdx.b\"),C=Et(\"sourceLocA\",m-1).concat(\"inIdx.a\"),w=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${x.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${C.join()})));`,_=`vec4(\n            getAChannel(${g.join()}),\n            hasNextCol ? getAChannel(${x.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,E=n?\"\":`\n      float getBestIndicesAChannel(${h.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${h.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${E}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${c[p-1]} < ${i[p-1]-1};\n        bool hasNextRow = ${c[p-2]} < ${i[p-2]-1};\n        ${l}\n        ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n          sourceLocB${f}, sourceLocA${f}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${_};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${k}\n          vec4 candidate = ${_};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${w}(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 mA(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new sh(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,\"int32\");if(c.shape[1]===1)return c;let l=mA(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function dA(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=S.computeOptimalWindowSize(s),i=new ah(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,\"int32\");if(u.shape.length===e.shape.length){let c=dA(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function ih(r,e,t,o){let n=[t];if(S.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!O().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=S.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=re({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=mA(r,m,o);s.push(d);let f=re({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return dA(r,e,o)}function bY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var fA={kernelName:Bo,backendName:\"webgl\",kernelFunc:bY};function CY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var hA={kernelName:ei,backendName:\"webgl\",kernelFunc:CY};var SY=zt+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,wY=he({opSnippet:SY}),gA={kernelName:ua,backendName:\"webgl\",kernelFunc:wY};var IY=zt+\"return log(x + sqrt(x * x + 1.0));\",vY=he({opSnippet:IY}),xA={kernelName:pa,backendName:\"webgl\",kernelFunc:vY};var kY=zt+`\n  return atan(x);\n`,NY=he({opSnippet:kY}),yA={kernelName:ca,backendName:\"webgl\",kernelFunc:NY};var TY=dc+`\n  return atan(a, b);\n`,_Y=`\n  vec4 result = atan(a, b);\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  `+Js+`\n  return result;\n`,$Y=tt({opSnippet:TY,packedOpSnippet:_Y}),bA={kernelName:ma,backendName:\"webgl\",kernelFunc:$Y};var EY=zt+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,AY=he({opSnippet:EY}),CA={kernelName:la,backendName:\"webgl\",kernelFunc:AY};var cs=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 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${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 ${A} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let C=\"max\",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(w=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,E=`\n      if (${h}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${C}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${p});\n      const ivec2 pads = ivec2(${d}, ${f});\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 < ${l};\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 < ${k}; wC += 4) {\n            int xC = xCCorner + wC * ${c};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${c}, d),\n              getValue(batch, xR, xC + 2 * ${c}, d),\n              getValue(batch, xR, xC + 3 * ${c}, d)\n            );\n\n            ${E}\n          }\n\n          int xC = xCCorner + ${k};\n          if (${_===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${E}\n          } else if (${_===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${c}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${E}\n          } else if (${_===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${c}, d),\n              getValue(batch, xR, xC + 2 * ${c}, d),\n              initializationValue\n            );\n\n            ${E}\n          }\n        }\n        setOutput(${w});\n      }\n    `}},Wi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",w=\"0.0\";if(C||(w=\"-1.0 / 1e-20\"),o){let D=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${p}, ${u});\n        const ivec3 pads = ivec3(${g}, ${x}, ${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 += ${c}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${f};\n                wR += ${l}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${h};\n                  wC += ${m}) {\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 ${D} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n                      wR * ${h} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let E=Math.floor(a/4)*4,A=a%4,R=`\n      if (${C}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${k}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${p}, ${u});\n      const ivec3 pads = ivec3(${g}, ${x}, ${b});\n      const float initializationValue = ${w};\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 >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${w});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${c}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${f};\n            wR += ${l}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${E}; wC += 4) {\n              int xC = xCCorner + wC * ${m};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${m}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n              );\n\n              ${R}\n            }\n\n            int xC = xCCorner + ${E};\n            if (${A===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${R}\n            } else if (${A===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${m}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${R}\n            } else if (${A===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${m}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n                initializationValue\n              );\n\n              ${R}\n            }\n          }\n        }\n        setOutput(${_});\n      }\n    `}};function RY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;us(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new cs(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var SA={kernelName:Vo,backendName:\"webgl\",kernelFunc:RY};function DY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new Wi(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var wA={kernelName:dp,backendName:\"webgl\",kernelFunc:DY};var uh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n      const ivec2 pads = ivec2(${c}, ${l});\n      const float avgMultiplier = float(${m});\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 < ${p};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${n}.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) / ${s}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},ph=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n      const ivec3 pads = ivec3(${f}, ${h}, ${g});\n      const float avgMultiplier = float(${x});\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 < ${l};\n            wD += ${p}) {\n          float dyD = float(dyDCorner + wD) / ${s}.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 < ${m};\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 += ${c}) {\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 FY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new ph(m);return t.runWebGLProgram(d,[n],a.dtype)}var IA={kernelName:vm,backendName:\"webgl\",kernelFunc:FY};function OY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;us([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new uh(c);return t.runWebGLProgram(l,[n],a.dtype)}var vA={kernelName:mp,backendName:\"webgl\",kernelFunc:OY};function PY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return zu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var kA={kernelName:zo,backendName:\"webgl\",kernelFunc:PY};var ch=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${i};\n        float scale = ${p};\n        float inv = scale * inversesqrt(variance + float(${a}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `}};var lh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 offset = ${i};\n        vec4 scale = ${p};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `}};var MY=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new lh(o.shape,n.shape,s.shape,c,l,p):new ch(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},NA={kernelName:sn,backendName:\"webgl\",kernelFunc:MY};var mh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=$e(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=LY(this.rank),n,s=e.map((a,i)=>`sourceLoc.${Lw[i]} = start[${i}] + coords.${Lw[i]};`);n=`\n        ${t} sourceLoc;\n        ${t} coords = getOutputCoords();\n        ${s.join(`\n`)}\n      `,this.userCode=`\n      void main() {\n        ${n}\n        setOutput(getSource(${o}));\n      }\n    `}},Lw=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function LY(r){if(r===1)return\"sourceLoc\";if(r<=6)return Lw.slice(0,r).map(e=>\"sourceLoc.\"+e).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var dh=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=$e(this.rank),o=Et(\"coords\",this.rank),n=Et(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n      result.x = ${a};\n      if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n        ++${n[this.rank-1]};\n        result.y = ${a};\n        --${n[this.rank-1]};\n      }\n    `,p=this.rank===1?\"\":`\n      --${o[this.rank-1]};\n      if (++${o[this.rank-2]} < ${e[this.rank-2]}) {\n        ++${n[this.rank-2]};\n        result.z = ${a};\n        if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n          ++${n[this.rank-1]};\n          result.w = ${a};\n        }\n      }\n    `,u=this.rank<=4?`sourceLoc = coords +\n            ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n      void main() {\n        ${t} coords = getOutputCoords();\n        ${t} sourceLoc;\n        ${u}\n        vec4 result = vec4(0.);\n        ${i}\n        ${p}\n        setOutput(result);\n      }\n    `}};function BY(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=ut.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function ls(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.texData.get(n.dataId),m=xE(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=ut.isSliceContinous(n.shape,i,p);if(u||!c){let l=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new dh(p):new mh(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),BY(n,i,p,t)}var TA={kernelName:Es,backendName:\"webgl\",kernelFunc:ls};var VY=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=re({inputs:{x:n},backend:t,attrs:{shape:p}}),h=yt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=re({inputs:{x:h},backend:t,attrs:{shape:c}}),x=ls({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},_A={kernelName:bs,backendName:\"webgl\",kernelFunc:VY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=Kf(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var $A={kernelName:ti,backendName:\"webgl\",kernelFunc:zY};function WY(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=S.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return 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a=Ka({inputs:{input:n},backend:t}),i=Vw({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=W$(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return DA(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=Bw({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var FA={kernelName:co,backendName:\"webgl\",kernelFunc:Vw};var OA=\"return ceil(x);\",HY=he({opSnippet:OA,packedOpSnippet:OA,cpuKernelImpl:U$}),PA={kernelName:Wo,backendName:\"webgl\",kernelFunc:HY};var fh=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `}};var hh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `}};function KY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;O().getBool(\"WEBGL_PACK_CLIP\")?i=new hh(n.shape):i=new fh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var MA={kernelName:lo,backendName:\"webgl\",kernelFunc:KY};var gh=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `}};function LA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function qY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new gh(o.shape),a=[LA(o,n.complexTensorInfos.real),LA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var BA={kernelName:hp,backendName:\"webgl\",kernelFunc:qY};var xh=class{constructor(e){this.outputShape=[],this.outputShape=S.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 o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${o.join(`\n        `)}\n      }\n    `}};var bh=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let o=this.outputShape,n=o.length,s=$e(n),a=Et(\"coords\",n),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,n);this.variableNames=e.map((h,g)=>`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h<p.length;h++)p[h]=p[h-1]+e[h][t];let u=i[t],c=i.slice(-2),l=i.join(),m=`if (${u} < ${p[0]}) {\n        return getChannel(\n            getT0(${l}), vec2(${c.join()}));\n        }`;for(let h=1;h<p.length;h++){let g=p[h-1];m+=`\n        if (${u} < ${p[h]}  && ${u} >= ${p[h-1]}) {\n          return getChannel(\n            getT${h}(${yh(i,u,g)}),\n            vec2(${yh(c,u,g)}));\n        }`}let d=p.length,f=p[p.length-1];m+=`\n        return getChannel(\n          getT${d}(${yh(i,u,f)}),\n          vec2(${yh(c,u,f)}));`,this.userCode=`\n      float getValue(${i.map(h=>\"int \"+h)}) {\n        ${m}\n      }\n\n      void main() {\n        ${s} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[n-1]} = ${a[n-1]} + 1;\n        if (${a[n-1]} < ${o[n-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[n-2]} = ${a[n-2]} + 1;\n        if (${a[n-2]} < ${o[n-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[n-1]} = ${a[n-1]} - 1;\n        if (${a[n-2]} < ${o[n-2]} &&\n            ${a[n-1]} < ${o[n-1]}) {\n          result.b = getValue(${a});\n        }\n        setOutput(result);\n      }\n    `}};function yh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Wu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:t})}var VA={kernelName:ci,backendName:\"webgl\",kernelFunc:Wu};function hc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>Ka({inputs:{input:b},backend:t})),f=r.map(b=>Wu({inputs:{input:b},backend:t})),h=hc(d,e,t),g=hc(f,e,t),x=Ar({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(w=>{let _=[-1,y.sizeFromShape(w.shape.slice(e))];return re({inputs:{x:w},backend:t,attrs:{shape:_}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape})),h=S.computeOutShape(d.map(w=>w.shape),1),g=d[0].shape[0]===1,x=G$(f,h,o,g),b=S.computeOutShape(r.map(w=>w.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(w=>t.disposeIntermediateTensorInfo(w)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new Jt(r[0].shape,Zs):new Er(r[0].shape,Zs);return t.runWebGLProgram(d,r,o)}let i=O().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;h<s.length;h+=i){let g=s.slice(h,h+i);d.push(hc(g,e,t))}let f=hc(d,e,t);for(let h of d)t.disposeIntermediateTensorInfo(h);return f}if(a){let d=new bh(s.map(f=>f.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=jY(s,e,t),c=new xh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=re({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function jY(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>re({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function zw(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):hc(p,s,t)}var zA={kernelName:Cs,backendName:\"webgl\",kernelFunc:zw};var gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,w=\"\",k=\"\";o&&(n?w=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${o}\n        }`:s?w=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${o}\n        }`:w=`\n          float activation(float x) {\n            ${o}\n          }\n        `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${w}\n\n      const ivec2 strides = ivec2(${p}, ${u});\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${C}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${x}], coords[${b}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${m}; wR++) {\n          int xR = xRCorner + wR * ${c};\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 < ${f}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${g}) {\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 (${h===1}) {\n\n              if (${g}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${f}) *\n                    getW(wR, wC, ${f}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${f}, xR, xC) *\n                    getW(wR, wC, ${f}, d2);\n              }\n\n            } else if (${h===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${f}, d2),\n                getW(wR, wC, ${f} + 1, d2)\n              );\n\n              if (${g}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${f}),\n                  getX(batch, xR, xC, ${f} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${f}, xR, xC),\n                  getX(batch, ${f} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${h===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${f}, d2),\n                getW(wR, wC, ${f} + 1, d2),\n                getW(wR, wC, ${f} + 2, d2)\n              );\n\n              if (${g}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${f}),\n                  getX(batch, xR, xC, ${f} + 1),\n                  getX(batch, xR, xC, ${f} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${f}, xR, xC),\n                  getX(batch, ${f} + 1, xR, xC),\n                  getX(batch, ${f} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${_}\n        ${k}\n        setOutput(result);\n      }\n    `}},Ch=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${s}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${o}, ${n});\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 < ${l}; wF++) {\n          int xF = xFCorner + wF * ${p};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${m}; 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 * ${c};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${f}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${h===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${f}) *\n                  getW(wF, wR, wC, ${f}, d2);\n              } else if (${h===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${f}),\n                  getX(batch, xF, xR, xC, ${f} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${f}, d2),\n                  getW(wF, wR, wC, ${f} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${h===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${f}),\n                  getX(batch, xF, xR, xC, ${f} + 1),\n                  getX(batch, xF, xR, xC, ${f} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${f}, d2),\n                  getW(wF, wR, wC, ${f} + 1, d2),\n                  getW(wF, wR, wC, ${f} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};var xc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n       int xR; int xC; int xCOffset;\n       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)m+=`\n           vec4 xTexelC${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};`;m+=`\n     for (int r = 0; r < ${u}; r++) {\n      for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {\n       `;for(let g=0;g<c;g++)m+=`\n           xTexelC${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);`;m+=`\n         xR = xRCorner + r * dilations[0];\n         if (xR >=0 && xR < inDims[0]) {\n       `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n           xC = xCCorner + ${x*p};\n           `,i===1){if(x<c&&(a%2===1?(m+=`\n                 xCOffset = xC + 1;\n                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n                   xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${x}.zw = vec2(0.0);\n                   }\n                   xTexelC${x}Ready = 1;\n                 }\n               `,p===1&&x>0?m+=`\n                 xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n                 `:m+=`\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${x} = vec4(previous.zw, xTexelC${x}.xy);\n                   } else {\n                     xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n                   }\n                   `):m+=`\n                 if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n                   xTexelC${x} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${x}.zw = vec2(0.0);\n                   }\n                   xTexelC${x}Ready = 1;\n                 }\n\n                 xC${x} = xTexelC${x};\n                 `,x+1<c)){let b=a%2===0?y.nearestLargerEven(p):p;p%2===0&&a%2===1||p%2!==0&&a%2!==1?(m+=`\n                   xCOffset = xC + imod(pads[1], 2) + ${b};\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n                     xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       xTexelC${x+1}.zw = vec2(0.0);\n                     }\n                     xTexelC${x+1}Ready = 1;\n                   }\n                   `,p>1?m+=`\n                     xCOffset -= 2;\n                     if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                      previous = getX(batch, xR, xCOffset, d1);\n                      xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n                     } else {\n                      xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n                     }\n                     `:m+=`\n                     xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n                     `):b===1?m+=`\n                     xC${x+1} = xTexelC${x};\n                     `:m+=`\n                     xCOffset = xC + ${b};\n\n                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n                       xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n                       if (xCOffset + 1 >= inDims[1]) {\n                         xTexelC${x+1}.zw = vec2(0.0);\n                       }\n                       xTexelC${x+1}Ready = 1;\n                     }\n\n                     xC${x+1} = xTexelC${x+1};\n                     `}}else x<c&&(a%2===1?(m+=`\n                 xCOffset = xC + 1 - strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n                   xTexelC${x} = getX(batch, xR, xCOffset, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${x}.zw = vec2(0.0);\n                   }\n                   xTexelC${x}Ready = 1;\n                 }\n\n                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n                   xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xC + 2 >= inDims[1]) {\n                     xTexelC${x+1}.zw = vec2(0.0);\n                   }\n                   xTexelC${x+1}Ready = 1;\n                 }\n\n                 xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n               `,x+1<c&&(m+=`\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${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n                 `)):(m+=`\n                 if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n                   xTexelC${x} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${x}.zw = vec2(0.0);\n                   }\n                   xTexelC${x}Ready = 1;\n                 }\n\n                 xCOffset = xC + strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n                   xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${x+1}.zw = vec2(0.);\n                   }\n                   xTexelC${x+1}Ready = 1;\n                 }\n\n                 xC${x} = vec4(\n                   xTexelC${x}.xy, xTexelC${x+1}.xy);\n               `,x+1<c&&(m+=`\n                   xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n                 `)));x<c&&(m+=`\n             wTexel = getW(r, ${x}, d1, d2);\n             dotProd += xC${x}.xxzz * vec4(wTexel.xy, wTexel.xy);\n             if(d1 + 1 < ${e.inChannels}) {\n               dotProd += xC${x}.yyww * vec4(wTexel.zw, wTexel.zw);\n             }\n           `,x+1<c&&(m+=`\n               wTexel = getW(r, ${x+1}, d1, d2);\n               dotProd += xC${x+1}.xxzz * vec4(wTexel.xy, wTexel.xy);\n               if(d1 + 1 < ${e.inChannels}) {\n                 dotProd += xC${x+1}.yyww * vec4(wTexel.zw, wTexel.zw);\n               }\n             `))}m+=`\n     }\n   `,m+=`\n     }\n   `,m+=`\n     }\n   `;let d=\"\",f=\"\";o&&(n?d=`vec4 activation(vec4 a) {\n           vec4 b = getPreluActivationWeightsAtOutCoords();\n           ${o}\n         }`:s?d=`vec4 activation(vec4 a) {\n           vec4 b = getLeakyreluAlphaAtOutCoords();\n           ${o}\n         }`:d=`vec4 activation(vec4 x) {\n           ${o}\n         }`,f=\"result = activation(result);\");let h=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n       ${d}\n\n       void main() {\n         ivec4 coords = getOutputCoords();\n         int batch = coords.x;\n         ivec2 xRCCorner = coords.yz * strides - pads;\n         int d2 = coords.w;\n         int xRCorner = xRCCorner.x;\n         int xCCorner = xRCCorner.y;\n\n         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n         vec4 dotProd = vec4(0.000000000000001);\n\n         ${m}\n\n         vec4 result = dotProd - vec4(0.000000000000001);\n         ${h}\n         ${f}\n         setOutput(result);\n       }\n     `}};var Sh=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec4\"},{name:\"pad\",type:\"ivec2\"},{name:\"stride\",type:\"ivec2\"},{name:\"dilation\",type:\"ivec2\"},{name:\"inChannels\",type:\"int\"},{name:\"itemsPerBlockRow\",type:\"int\"},{name:\"outWidth\",type:\"int\"}],this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let{dataFormat:o}=t,n=wt(),s=o===\"channelsLast\",a=s?1:2,i=s?2:3,p=this.enableShapeUniforms?\"if(blockIndex < outShape[2] && pos < outShape[1]) {\":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,u=\"\";for(let c=0;c<=1;c++)for(let l=0;l<=1;l++)u+=`\n          blockIndex = rc.z + ${l};\n          pos = rc.y + ${c};\n\n          ${p}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${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[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${s}) {\n                  innerDims = vec2(d1, ch);\n                  result[${c*2+l}] = getChannel(\n                    getA(rc.x, d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${c*2+l}] = getChannel(\n                    getA(rc.x, ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${u}\n\n        ${n.output} = result;\n      }\n    `}};function wh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Ih({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let w=wh(s.shape,d);w!=null&&(s=re({inputs:{x:s},backend:o,attrs:{shape:w}}),x.push(s))}if(n!=null){let w=wh(n.shape,d);w!=null&&(n=re({inputs:{x:n},backend:o,attrs:{shape:w}}),x.push(n))}if(!((l===1||m===1)&&c>Mw)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let w=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Bi(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=re({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(E);let A=zu({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),R=o.texData.get(A.dataId);y.assert(R.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,R.shape=t.outShape,g=At({inputs:{x:A},backend:o}),g.shape=t.outShape,x.push(A)}else{let w=t.outHeight*t.outWidth,k=re({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,w,t.inChannels]:[t.batchSize,t.inChannels,w]}}),_=re({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),E=zu({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=re({inputs:{x:E},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push(E)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,w=[];if(s!=null){let H=wh(s.shape,f);H!=null&&(s=re({inputs:{x:s},backend:o,attrs:{shape:H}}),w.push(s))}if(n!=null){let H=wh(n.shape,f);H!=null&&(n=re({inputs:{x:n},backend:o,attrs:{shape:H}}),w.push(n))}let k=re({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(k);let _=new Sh(x,t),E=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],A=o.runWebGLProgram(_,[r],\"float32\",E),R=re({inputs:{x:A},backend:o,attrs:{shape:x}});w.push(A),w.push(R);let D=n!=null,P=s!=null,M=i===\"leakyrelu\",L=i?Ha(i,!0):null,V=new fc(f?R.shape:k.shape,f?k.shape:R.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,D,L,P,M),z=f?[R,k]:[k,R];if(n&&z.push(n),P&&z.push(s),M){let H=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(H),w.push(H)}let U=o.runWebGLProgram(V,z,\"float32\"),K=re({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});w.push(U);for(let H of w)o.disposeIntermediateTensorInfo(H);return K}function XY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=Ih({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let h=new xc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))d=vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new gc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=re({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var WA={kernelName:Uo,backendName:\"webgl\",kernelFunc:XY};var kh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=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} - ${n};\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 * ${o} - ${s};\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    `}},Nh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${l}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - 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) / ${n}.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 < ${o}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${s}.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 = ${o} - 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    `}},Th=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=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} - ${s};\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 * ${o} - ${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 * ${n} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},_h=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${p}, ${u}, ${c});\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) / ${s}.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 < ${o}; 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 = ${o} - 1 - wR;\n\n            for (int wC = 0; wC < ${n}; 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 = ${n} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function YY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new kh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var UA={kernelName:oi,backendName:\"webgl\",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=new Nh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var GA={kernelName:Go,backendName:\"webgl\",kernelFunc:QY};function ZY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ch(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var HA={kernelName:gp,backendName:\"webgl\",kernelFunc:ZY};function JY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=S.computeConv3DInfo(n.shape,p,a,1,i),c=new Th(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var KA={kernelName:km,backendName:\"webgl\",kernelFunc:JY};function eQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=S.computeConv3DInfo(p,s.shape,i,1,a),c=new _h(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var qA={kernelName:xp,backendName:\"webgl\",kernelFunc:eQ};var tQ=$o+`\n  return cos(x);\n`,rQ=he({opSnippet:tQ}),jA={kernelName:Ho,backendName:\"webgl\",kernelFunc:rQ};var oQ=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,nQ=he({opSnippet:oQ}),XA={kernelName:Ko,backendName:\"webgl\",kernelFunc:nQ};var $h=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,w,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n      const float height_ratio = float(${g});\n      const float width_ratio = float(${C});\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 = ${x};\n        float width_scale = ${w};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${f} ) {\n          setOutput(float(${s}));\n          return;\n        }\n        float in_x = ${k};\n        if( in_x < 0.0 || in_x > ${h} ) {\n          setOutput(float(${s}));\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    `}};var sQ=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new $h(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},YA={kernelName:Xo,backendName:\"webgl\",kernelFunc:sQ};var Uu;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Uu||(Uu={}));var Rl=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Uu.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${QA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${$e(s)} coords = getOutputCoords();\n        int end = ${ZA(s,\"coords\",this.op)};\n        float val = ${i};\n        int pow2 = int(pow(2.0, index));\n        if (${u}) {\n          int idx = ${c};\n          ${ZA(s,\"coords\",this.op)} = idx;\n          val ${this.op}= getX(${QA(s,\"coords\",this.op)});\n        }\n        setOutput(val);\n      }\n    `}};function QA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function ZA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Eh(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=yt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Rl(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new Rl(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=yt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function aQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Eh(Uu.Prod,n,t,s,a,i)}var JA={kernelName:qo,backendName:\"webgl\",kernelFunc:aQ};function iQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Eh(Uu.Sum,n,t,s,a,i)}var eR={kernelName:jo,backendName:\"webgl\",kernelFunc:iQ};function uQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=Kf(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=z$(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var tR={kernelName:ni,backendName:\"webgl\",kernelFunc:uQ};var Ah=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,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 pQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Ah(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var rR={kernelName:Yo,backendName:\"webgl\",kernelFunc:pQ};var yc=class{constructor(e,t=!1,o=null,n=!1,s=!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=lt(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${o}\n        }`:s?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${o}\n        }`:u=`\n          float activation(float x) {\n            ${o}\n          }\n        `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&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 / ${p};\n        int q = d2 - d1 * ${p};\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        ${l}\n        ${c}\n        setOutput(result);\n      }\n    `}};var bc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x<l;x++)d+=`\n          vec4 xTexelC${x*2};\n          int xTexelC${x*2}Ready;\n          vec4 xTexelC${x*2+1};\n          int xTexelC${x*2+1}Ready;\n          vec4 xC${x};`;d+=`\n    for (int r = 0; r < ${c}; r++) {\n      `;for(let x=0;x<l;x++)d+=`\n          xTexelC${x*2} = vec4(0.0);\n          xTexelC${x*2}Ready = 0;\n          xTexelC${x*2+1} = vec4(0.0);\n          xTexelC${x*2+1}Ready = 0;\n          xC${x} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,p===1){if(b<l&&(i%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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<l)){let C=i%2===0?y.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${C};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${b+1}Ready = 1;\n                  }\n                  `,u>1?d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n                     xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n                    } else {\n                     xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n                    }\n                    `:d+=`\n                    xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                    `):C===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${C};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${b+1};\n                    `}}else b<l&&(i%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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<l&&(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${b+1}.xy);\n              `,b+1<l&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<l&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<l&&(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 f=\"\",h=\"\";o&&(n?f=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${o}\n        }`:s?f=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${o}\n        }`:f=`vec4 activation(vec4 x) {\n          ${o}\n        }`,h=\"result = activation(result);\");let g=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${f}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${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        ${g}\n        ${h}\n        setOutput(result);\n      }\n    `}};function cQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p,dimRoundingMode:u}=o,c=p;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;O().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new bc(l):m=new yc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var oR={kernelName:Qo,backendName:\"webgl\",kernelFunc:cQ};var Rh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=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} - ${n};\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 * ${o} - ${s};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},Dh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${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) / ${n}.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 < ${o}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${s}.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 = ${o} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${p}; dm++) {\n              int d2 = d1 * ${p} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function lQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=S.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Rh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var nR={kernelName:yp,backendName:\"webgl\",kernelFunc:lQ};function mQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Dh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var sR={kernelName:bp,backendName:\"webgl\",kernelFunc:mQ};var Fh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function dQ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=re({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Fh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=re({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var aR={kernelName:si,backendName:\"webgl\",kernelFunc:dQ};var Oh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n      const ivec2 strides = ivec2(${s}, ${a});\n      const ivec2 pads = ivec2(${l}, ${m});\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 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m=re({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var iR={kernelName:ai,backendName:\"webgl\",kernelFunc:fQ};function hQ(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h<l;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:b}=S.getEinsumPermutation(d,p[g]),C;S.isIdentityPermutation(x)?C=s[g]:(C=yt({inputs:{x:s[g]},backend:t,attrs:{perm:x}}),f.push(C));let w=C.shape.slice();for(let k=0;k<b.length;++k)w.splice(b[k],0,1);y.arraysEqual(C.shape,w)||(C=re({inputs:{x:C},backend:t,attrs:{shape:w}}),f.push(C)),m===null?m=C:(m=El({inputs:{a:C,b:m},backend:t}),f.push(m))}h<l-1&&(u[h]>=0&&(m=Vu({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of 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`}};function Mh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=re({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new Dl(\"real\",p,e),c=new Dl(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Ar({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=re({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function EQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!1,t)}var xR={kernelName:ui,backendName:\"webgl\",kernelFunc:EQ};var Lh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `}};function qa(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Lh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var yR={kernelName:ws,backendName:\"webgl\",kernelFunc:qa};var Bh=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${t} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}};var bR={kernelName:rn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bh(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var CR=\"return floor(x);\",AQ=he({opSnippet:CR,packedOpSnippet:CR,cpuKernelImpl:j$}),SR={kernelName:on,backendName:\"webgl\",kernelFunc:AQ};var RQ=`\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`,DQ=`\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`,FQ=tt({opSnippet:RQ,packedOpSnippet:DQ,dtype:\"int32\"}),wR={kernelName:nn,backendName:\"webgl\",kernelFunc:FQ};var Vh=class{constructor(e){this.variableNames=[\"A\"];let t=wt(),[o,n]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n        vec4 values = ${t.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if 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values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${t.output} = result;\n      }\n    `}};var IR={kernelName:Zi,backendName:\"webgl\",kernelFunc:OQ},Cc,Uw=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function OQ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Cc==null||h!==Uw)&&(Uw=h,Cc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Uw})),Cc.canvas.width=p,Cc.canvas.height=u,Cc.drawImage(n,0,0,p,u),n=Cc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=ur.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=O().getBool(\"WEBGL_PACK\")?new zh(l):new Vh(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function PQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,w=i!=null,k=d===\"leakyrelu\",_=()=>{let A=[n,s],R=(D,P)=>{if(P===\"NCHW\"&&D.shape.length===1&&D.shape[0]!==1){let M=re({inputs:{x:D},backend:t,attrs:{shape:[D.shape[0],1,1]}});return b.push(M),M}return D};if(C&&A.push(R(a,c)),w&&A.push(R(i,c)),k){let D=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));A.push(D),b.push(D)}return 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\"))x=Ih({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let A=d?Ha(d,!0):null,R=new xc(g,C,A,w,k),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],P=_();x=t.runWebGLProgram(R,P,\"float32\",D)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))x=vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let A=d?Ha(d,!1):null,R=new gc(g,C,A,w,k),D=_();x=t.runWebGLProgram(R,D,\"float32\")}let E=re({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(A=>t.disposeIntermediateTensorInfo(A)),E}var vR={kernelName:go,backendName:\"webgl\",kernelFunc:PQ};function MQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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out_of_bounds = out_of_bounds || index < 0;\n          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[i]};\n          flattenIndex += index * ${this.strides[i]};`;this.userCode=`\n         void main() {\n          ${s} coords = getOutputCoords();\n          int flattenIndex = 0;\n          bool out_of_bounds = false;\n\n          ${a}\n\n          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n        }\n      `}};function LQ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=S.prepareAndValidate(o,n),m=re({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=re({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=t.readSync(n.dataId),b=t.bufferSync(o),C=X$(x,b,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,C.values)}let f=new Wh(a,l,[u,c],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=re({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var NR={kernelName:an,backendName:\"webgl\",kernelFunc:LQ};var Uh=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=$e(this.rank),n=BQ(e,2);this.userCode=`\n      void main() {\n        ${o} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${n}));\n      }\n    `}};function BQ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n<r.length;n++)n===2?o.push(\"index\"):o.push(`${t[n]}`);return o.join()}function Gw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0];if(O().get(\"DEBUG\")){let b=t.readSync(s.dataId),C=n.shape[p];for(let w=0;w<b.length;++w){let k=b[w];y.assert(k<=C-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=re({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=re({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),w=Y$(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new Uh(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=re({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var TR={kernelName:Is,backendName:\"webgl\",kernelFunc:Gw};var VQ=\"return float(a > b);\",zQ=`\n  return vec4(greaterThan(a, b));\n`,WQ=tt({opSnippet:VQ,packedOpSnippet:zQ,cpuKernelImpl:Q$,dtype:\"bool\"}),_R={kernelName:un,backendName:\"webgl\",kernelFunc:WQ};var UQ=\"return float(a >= b);\",GQ=`\n  return vec4(greaterThanEqual(a, b));\n`,HQ=tt({opSnippet:UQ,packedOpSnippet:GQ,dtype:\"bool\",cpuKernelImpl:Z$}),$R={kernelName:pn,backendName:\"webgl\",kernelFunc:HQ};function KQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!0,t)}var ER={kernelName:pi,backendName:\"webgl\",kernelFunc:KQ};var qQ=\"return float(!isnan(x) && !isinf(x));\",jQ=he({opSnippet:qQ,dtype:\"bool\"}),AR={kernelName:ha,backendName:\"webgl\",kernelFunc:jQ};var XQ=\"return float(isinf(x));\",YQ=he({opSnippet:XQ,dtype:\"bool\"}),RR={kernelName:ga,backendName:\"webgl\",kernelFunc:YQ};var QQ=\"return float(isnan(x));\",ZQ=he({opSnippet:QQ,dtype:\"bool\"}),DR={kernelName:cn,backendName:\"webgl\",kernelFunc:ZQ};var JQ=\"return float(a < b);\",e7=`\n  return vec4(lessThan(a, b));\n`,t7=tt({opSnippet:JQ,packedOpSnippet:e7,cpuKernelImpl:J$,dtype:\"bool\"}),FR={kernelName:mn,backendName:\"webgl\",kernelFunc:t7};var r7=\"return float(a <= b);\",o7=`\n  return vec4(lessThanEqual(a, b));\n`,n7=tt({opSnippet:r7,packedOpSnippet:o7,cpuKernelImpl:eE,dtype:\"bool\"}),OR={kernelName:dn,backendName:\"webgl\",kernelFunc:n7};function s7(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=tE(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var PR={kernelName:li,backendName:\"webgl\",kernelFunc:s7};var a7=$o+`\n  return x < 0.0 ? 0./0. : log(x);\n`,i7=`\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`,u7=he({opSnippet:a7,packedOpSnippet:i7,cpuKernelImpl:rE}),MR={kernelName:fn,backendName:\"webgl\",kernelFunc:u7};var p7=$o+`\n  return log(1.0 + x);\n`,c7=he({opSnippet:p7}),LR={kernelName:xa,backendName:\"webgl\",kernelFunc:c7};var l7=\"return float(a >= 1.0 && b >= 1.0);\",m7=`\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`,d7=tt({opSnippet:l7,packedOpSnippet:m7,dtype:\"bool\"}),BR={kernelName:hn,backendName:\"webgl\",kernelFunc:d7};var f7=\"return float(!(x >= 1.0));\",h7=he({opSnippet:f7}),VR={kernelName:gn,backendName:\"webgl\",kernelFunc:h7};var g7=\"return float(a >= 1.0 || b >= 1.0);\",x7=`\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`,y7=tt({opSnippet:g7,packedOpSnippet:x7,dtype:\"bool\"}),zR={kernelName:xn,backendName:\"webgl\",kernelFunc:y7};var Gh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${a}; j <= ${a}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${i}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${p};\n        setOutput(val);\n      }\n    `}};var Hh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${a};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${a}; j <= ${a}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${p};\n        setOutput(result);\n      }\n    `}};var b7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new Hh(n.shape,s,a,i,p):new Gh(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},WR={kernelName:mi,backendName:\"webgl\",kernelFunc:b7};var Kh=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${n}) * norm + float(${o});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${n})\n                * float(${s})\n                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${s});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}};var C7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Kh(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},UR={kernelName:Tm,backendName:\"webgl\",kernelFunc:C7};function GR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=re({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Ur(i,r.dtype,\"max\",o),u=re({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Hw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,w=new Array(i);for(let E=0;E<w.length;E++)w[E]=n.shape[c[E]];let k=Bu(C,n.shape,n.dtype,c,w);d=t.makeTensorInfo(w,n.dtype);let _=t.texData.get(d.dataId);_.values=k}else d=zi(n,c,t);u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims(\"max\",u,i);let[f,h]=S.computeOutAndReduceShapes(d.shape,u),g=f;a&&(g=S.expandShapeToKeepDim(f,p));let x;if(m){let C=t.texData.get(d.dataId).values,w=oE(C,y.sizeFromShape(h),g,n.dtype);x=t.makeTensorInfo(g,n.dtype);let k=t.texData.get(x.dataId);k.values=w}else x=GR(d,h,g,t);return l&&t.disposeIntermediateTensorInfo(d),x}var HR={kernelName:yn,backendName:\"webgl\",kernelFunc:Hw};var S7=dc+`\n  return max(a, b);\n`,w7=`\n  vec4 result = vec4(max(a, b));\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  `+Js+`\n  return result;\n`,I7=tt({opSnippet:S7,packedOpSnippet:w7,cpuKernelImpl:nE}),KR={kernelName:bn,backendName:\"webgl\",kernelFunc:I7};function v7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;us(n,\"maxPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new cs(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var qR={kernelName:Cn,backendName:\"webgl\",kernelFunc:v7};function k7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Wi(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var jR={kernelName:Cp,backendName:\"webgl\",kernelFunc:k7};var qh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${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 < ${s};\n          wR += ${n}) {\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) / ${o}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(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 * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},jh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n      const ivec3 pads = ivec3(${l}, ${m}, ${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        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${p};\n           wD += ${s}) {\n          float dyD = float(dyDCorner + wD) / ${t}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${u};\n              wR += ${a}) {\n            float dyR = float(dyRCorner + wR) / ${o}.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 < ${c};\n                wC += ${i}) {\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(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${f} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${u} * ${c} +\n                  wR * ${c} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function N7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new Wi(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new jh(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var XR={kernelName:$m,backendName:\"webgl\",kernelFunc:N7};function T7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;us([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=S.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new cs(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new qh(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var YR={kernelName:_m,backendName:\"webgl\",kernelFunc:T7};function QR(r,e,t,o){let n=new cs(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new cs(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var ZR={kernelName:Sp,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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setOutput(value);\n        } else {\n          ${s} coords = outC - start;\n          setOutput(getX(${p}));\n        }\n      }\n    `}};var eg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=$e(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=Et(\"rc\",n),u=Et(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n       if(${c}) {\n      `,n===1?\"\":`}\n       rc = outputLoc;\n       ${p[n-2]} += 1;\n       if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":`  ${p[n-1]} += 1;\n         if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h<g;h++)f+=`\n        ${m[h]}\n        if (${d}) {\n          result[${h}] = float(value);\n        } else {\n          ${s} source = rc - start;\n          result[${h}] = getChannel(getX(${u.join()}), ${l});\n        }\n      `;f+=n===1?\"} \":\"}}\",this.userCode=`\n      const ${s} start = ${s}(${a});\n      const ${s} end = ${s}(${i});\n\n      void main() {\n        ${s} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${f}\n        setOutput(result);\n      }\n    `}};var Xw=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return qa({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new eg(n.shape,s,a):new Jh(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},bD={kernelName:En,backendName:\"webgl\",kernelFunc:Xw};var Y7=`\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,Q7=`\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  bvec4 isNaN1 = lessThan(a, vec4(0.0));\n  bvec4 isNaN2 = lessThan(floor(b), b);\n  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n  `+Js+`\n  return result;\n`,Z7=tt({opSnippet:Y7,packedOpSnippet:Q7}),CD={kernelName:An,backendName:\"webgl\",kernelFunc:Z7};function J7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=S.getAxesPermutation(c,i),m=n;l!=null&&(m=yt({inputs:{x:n},backend:t,attrs:{perm:l}}),c=S.getInnerMostAxes(c.length,i),p.push(m)),S.assertAxesAreInnerMostDims(\"prod\",c,i);let d;if(t.shouldExecuteOnCPU([m])){let f=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=pE(m.shape,m.dtype,f,c);d=t.makeTensorInfo(g,x,h)}else{let[f,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=re({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=Ta(n.dtype),C=Ur(x,b,\"prod\",t);d=re({inputs:{x:C},backend:t,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=S.expandShapeToKeepDim(d.shape,u);d=re({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var SD={kernelName:Dn,backendName:\"webgl\",kernelFunc:J7};function eZ(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=cE(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var wD={kernelName:Ip,backendName:\"webgl\",kernelFunc:eZ};function tZ(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lE(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var ID={kernelName:vp,backendName:\"webgl\",kernelFunc:tZ};function rZ(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=mE(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var vD={kernelName:kp,backendName:\"webgl\",kernelFunc:rZ};var Yw=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=dE(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},kD={kernelName:Ts,backendName:\"webgl\",kernelFunc:Yw};var oZ=\"return 1.0 / x;\",nZ=he({opSnippet:oZ}),ND={kernelName:Fn,backendName:\"webgl\",kernelFunc:nZ};var sZ=zt+`\n  return (x < 0.0) ? 0.0 : x;\n`,aZ=`\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`,iZ=he({opSnippet:sZ,packedOpSnippet:aZ}),TD={kernelName:On,backendName:\"webgl\",kernelFunc:iZ};var uZ=zt+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,pZ=`\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`,cZ=he({opSnippet:uZ,packedOpSnippet:pZ}),_D={kernelName:Ln,backendName:\"webgl\",kernelFunc:cZ};var tg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${c[0]/l[0]},\n          ${c[1]/l[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${p}.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 = ${m};\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    `}};var rg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${c[0]/l[0]},\n          ${c[1]/l[1]},\n          ${c[1]/l[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n                                     ${p}.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 = ${m};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${o-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function lZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new rg(n.shape,p,u,s,a):new tg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var $D={kernelName:Mn,backendName:\"webgl\",kernelFunc:lZ};var og=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${c});\n        const float widthScale = float(${l});\n\n        const float invHeightScale = float(${m});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${f});\n        const int winWidth = int(${h});\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), ${n-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), ${s-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 mZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new og(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var ED={kernelName:Am,backendName:\"webgl\",kernelFunc:mZ};var ng=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${c[0]/l[0]},\n          ${c[1]/l[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${p}.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 + ${m})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}};var sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${c[0]/l[0]},\n          ${c[1]/l[1]},\n          ${c[1]/l[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n                                     ${p}.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 + ${m})));\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 < ${o-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function dZ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new sg(n.shape,p,u,s,a):new ng(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var AD={kernelName:Pn,backendName:\"webgl\",kernelFunc:dZ};var ag=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${c});\n        const float widthScale = float(${l});\n\n        const float invHeightScale = float(${m});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${f});\n        const int winWidth = int(${h});\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 >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${p[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${p[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${n}) - 1),\n                ${o} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${s}) - 1),\n                ${o} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n     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ug=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Et(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=$e(o);o===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            ${e[0]} - rc - 1);\n          if(${s}){\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 = ${p(n.slice())};\n          if(${s}){\n            result.g = ${u(n.slice())};\n          }\n          if(${a}) {\n            result.b = ${c(n.slice())};\n            if(${s}) {\n              result.a = ${l(n.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function hZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:t});let p=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new ug(n.shape,i):new ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var DD={kernelName:Bn,backendName:\"webgl\",kernelFunc:hZ};var pg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n        vec3 fill = vec3(${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          ${s}\n          if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n            outputValue = getImage(coords[0], coordY, coordX, 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Sc=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let p=$e(s.length),u=$e(a.length),c=\"\";o===1?c=\"i\":o===2&&(c=\"i, j\");let l=`getIndices(${c})`,m=\"\";n===1?m=\"i\":n===2&&(m=\"i, coords[1]\");let d=`getUpdates(${m})`,f=t>1?\"strides[j]\":\"strides\";this.userCode=`\n        ${p} strides = ${p}(${s});\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 < ${t}; j++) {\n              int index = round(${l});\n              flattenedIndex += index * ${f};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${d};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `}};function CZ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=re({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=re({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new Sc(p,i,d.shape.length,f.shape.length,c,m),x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=re({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var MD={kernelName:Wn,backendName:\"webgl\",kernelFunc:CZ};var cg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) 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lg=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&p.push(`${i[c]}`);n=p.join(),s=u.join()}let a=$e(o);this.userCode=`\n      void main() {\n        ${a} resRC = getOutputCoords();\n        float cVal = getC(${n});\n        if (cVal >= 1.0) {\n          setOutput(getA(${s}));\n        } else {\n          setOutput(getB(${s}));\n        }\n      }\n    `}};function wZ(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new lg(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var BD={kernelName:$s,backendName:\"webgl\",kernelFunc:wZ};var IZ=`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = 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u=[],c=Xw({inputs:{x:n},backend:t,attrs:{paddings:p,constantValue:0}}),l=S.getReshaped(c.shape,s,i,!1),m=S.getPermuted(l.length,s.length,!1),d=S.getReshapedPermuted(c.shape,s,i,!1),f=re({inputs:{x:c},backend:t,attrs:{shape:l}}),h=yt({inputs:{x:f},backend:t,attrs:{perm:m}}),g=re({inputs:{x:h},backend:t,attrs:{shape:d}});return u.push(c),u.push(f),u.push(h),u.forEach(x=>t.disposeIntermediateTensorInfo(x)),g},KD={kernelName:As,backendName:\"webgl\",kernelFunc:PZ};function MZ(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n         ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n         ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n         ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n        ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=yE(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var qD={kernelName:hi,backendName:\"webgl\",kernelFunc:MZ};function LZ(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=bE(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var jD={kernelName:va,backendName:\"webgl\",kernelFunc:LZ};function BZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n              ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n              ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=jf(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var XD={kernelName:gi,backendName:\"webgl\",kernelFunc:BZ};function VZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new 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Sc(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=re({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var QD={kernelName:yi,backendName:\"webgl\",kernelFunc:zZ};function WZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=ls({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var ZD={kernelName:Rs,backendName:\"webgl\",kernelFunc:WZ};var JD=\"return sqrt(x);\",UZ=he({opSnippet:JD,packedOpSnippet:JD,cpuKernelImpl:CE}),eF={kernelName:Hn,backendName:\"webgl\",kernelFunc:UZ};var GZ=\"return x * x;\",HZ=he({opSnippet:GZ}),tF={kernelName:bi,backendName:\"webgl\",kernelFunc:HZ};var rF=\"return (a - b) * (a - b);\",KZ=tt({opSnippet:rF,packedOpSnippet:rF}),oF={kernelName:jn,backendName:\"webgl\",kernelFunc:KZ};function qZ({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=zt+`\n    return x > 0.0 ? 1.0 : float(${e.alpha});\n  `,s=new Jt(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var nF={kernelName:fo,backendName:\"webgl\",kernelFunc:qZ};var mg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=$e(o.length),a=$e(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n      ${s} begin = ${s}(${e});\n      ${s} strides = ${s}(${t});\n\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${i}));\n      }\n    `}};function jZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=re({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let E=ut.computeOutShape(b,C,w),A=ls({inputs:{x:n},backend:t,attrs:{begin:b,size:E}});k=re({inputs:{x:A},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(A)}else if(t.shouldExecuteOnCPU([n])){let A=t.readSync(n.dataId),R=le(n.shape,n.dtype,A),D=SE(d,R,w,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let A=new mg(b,w,d);k=t.runWebGLProgram(A,[n],n.dtype)}let _=re({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),_}var sF={kernelName:Xn,backendName:\"webgl\",kernelFunc:jZ};function XZ(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=wE(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var aF={kernelName:Ds,backendName:\"webgl\",kernelFunc:XZ};function YZ(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;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(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,c,l]=IE(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],\"int32\",u),t.makeTensorInfo([m],\"string\",c),t.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var iF={kernelName:Ci,backendName:\"webgl\",kernelFunc:YZ};function QZ(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=t.readSync(s.dataId),i=vE(a,n);return t.makeTensorInfo(s.shape,\"int32\",i)}var uF={kernelName:Si,backendName:\"webgl\",kernelFunc:QZ};var ZZ=\"return tan(x);\",JZ=he({opSnippet:ZZ}),pF={kernelName:Qn,backendName:\"webgl\",kernelFunc:JZ};var e9=`\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,t9=he({opSnippet:e9}),cF={kernelName:Zn,backendName:\"webgl\",kernelFunc:t9};var dg=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[a]*t[a];this.outputShape=o,this.rank=o.length;let n=$e(this.rank),s=r9(e);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        setOutput(getA(${s}));\n      }\n    `}};function r9(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n<r.length;n++)o.push(`imod(${t[n]}, ${r[n]})`);return o.join()}function Qw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;if(n.dtype===\"string\"||n.shape.length>5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=NE(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new dg(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var lF={kernelName:to,backendName:\"webgl\",kernelFunc:Qw};var fg=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     `}},hg=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 Gu(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function mF(r){let e=1;for(;e<r;)e*=2;return e}function o9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=O().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),p=O().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),u=n.shape,c=u[u.length-1];if(t.shouldExecuteOnCPU([n])||c<i||s>p){let D=t.readSync(n.dataId),[P,M]=TE(D,u,n.dtype,s,a);return[t.makeTensorInfo(P.shape,P.dtype,P.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,qa({attrs:{shape:u,dtype:\"int32\",value:0},backend:t})];let l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=re({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&Gu(t,d);let x=mF(s),b=mF(c),C=null,w=()=>C===null?[g,g]:[g,C],k=(D,P,M)=>{let L=w(),V=new fg(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[D],[P]],K=C;C=t.runWebGLProgram(V,L,\"int32\",U),Gu(t,K)};for(let D=1;D<x;D*=2){let P=D*2;for(let M=D;M>=1;M/=2)k(P,M,[h,b])}for(let D=b;D>x;D/=2){let P=w(),M=new hg([h,D/2]),V=[[c],[C===null?1:0],[x]],z=C;C=t.runWebGLProgram(M,P,\"int32\",V),Gu(t,z);let U=x/2,K=U*2;for(let H=U;H>=1;H/=2)k(K,H,C.shape)}let _=C;C=ls({inputs:{x:C},backend:t,attrs:{begin:0,size:[h,s]}}),Gu(t,_);let E=Gw({inputs:{x:g,indices:C},backend:t,attrs:{axis:1,batchDims:1}});Gu(t,g);let A=u.slice(0,-1);A.push(s),_=C,C=re({inputs:{x:C},attrs:{shape:A},backend:t}),Gu(t,_);let R=E;return E=re({inputs:{x:E},attrs:{shape:A},backend:t}),Gu(t,R),[E,C]}var dF={kernelName:Jn,backendName:\"webgl\",kernelFunc:o9};var gg=class{constructor(e,t,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${p} == 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 (${p} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${p} == 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 < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${s});\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(${s});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${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 n9(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new gg(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],\"float32\")}var fF={kernelName:es,backendName:\"webgl\",kernelFunc:n9};function s9(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;us(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=_E(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var hF={kernelName:Np,backendName:\"webgl\",kernelFunc:s9};function a9(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let l=[],m=new Array(i).fill(0),d=a.shape.slice();d[s]=1;let f=new Array(p);for(let h=0;h<f.length;h++){m[s]=h;let g=ls({inputs:{x:a},backend:t,attrs:{begin:m,size:d}}),x=re({inputs:{x:g},backend:t,attrs:{shape:u}});f[h]=x,l.push(g)}return l.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var gF={kernelName:Fs,backendName:\"webgl\",kernelFunc:a9};var xg=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";s%o>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${s}) {\n          return initializationValue;\n        }\n      `);let f=\"\";s%o>0&&(f=`\n        if (inIdx < 0 || inIdx >= ${s}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${p};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${f}\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          ${a})) * float(${o}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${c}; 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          ${m}\n        }\n\n        int inIdx = inOffset + ${c};\n        if (${l===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          ${m}\n        } else if (${l===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          ${m}\n        } else if (${l===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)) 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b.stringBytes=S.fromStringArrayToUint8(g),d.forEach(C=>t.disposeData(C.dataId)),i}let p=y.sizeFromShape(a[0].shape.slice(0,o)),u=0,c=a.map(d=>{let f=y.sizeFromShape(d.shape.slice(o));return u+=f,f}),l=a.map(d=>t.typedArrayFromHeap(d)),m=t.typedArrayFromHeap(i);for(let d=0;d<p;d++){let f=d*u;for(let h=0;h<l.length;h++){let g=c[h],x=d*g,b=l[h].subarray(x,x+g);m.set(b,f),f+=g}}return i}var GF={kernelName:Cs,backendName:\"wasm\",kernelFunc:Zw};var HF;function D9(r){HF=r.wasm.cwrap(Uo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function F9(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l,dataFormat:m}=t,d=S.convertConv2DDataFormat(m),f=S.computeConv2DInfo(n.shape,s.shape,p,u,c,l,!1,d),h=f.filterHeight,g=f.filterWidth,x=f.padInfo.top,b=f.padInfo.right,C=f.padInfo.bottom,w=f.padInfo.left,k=f.dilationHeight,_=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,R=f.inChannels,D=f.outChannels,P=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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P9(r){let{backend:e,inputs:t,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,inputShape:c}=o,l=1,m=S.convertConv2DDataFormat(p),d=S.computeConv2DInfo(c,s.shape,a,l,i,u,!1,m),{batchSize:f,filterHeight:h,filterWidth:g,inChannels:x,inHeight:b,inWidth:C,outChannels:w,outHeight:k,outWidth:_,strideHeight:E,strideWidth:A}=d,R=h-1-d.padInfo.top,D=g-1-d.padInfo.left,P=d.dataFormat===\"channelsLast\",M=y.computeStrides(d.inShape),L=y.computeStrides(n.shape),[V,z,U]=y.computeStrides(s.shape),K=M[0],H=P?M[1]:M[2],q=P?M[2]:1,Z=P?1:M[1],ee=L[0],oe=P?L[1]:L[2],J=P?L[2]:1,te=P?1:L[1],ie=e.makeOutput(d.inShape,\"float32\"),ce=e.dataIdMap.get(ie.dataId).id,ye=e.dataIdMap.get(n.dataId).id,Ne=e.dataIdMap.get(s.dataId).id;return qF(ye,Ne,f,h,g,b,C,x,k,_,w,E,A,R,D,V,z,U,K,H,q,Z,ee,oe,J,te,ce),ie}var jF={kernelName:Go,backendName:\"wasm\",setupFunc:O9,kernelFunc:P9};var XF=Ve(Ho);var YF=Ve(Ko);var 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G9(r){let{backend:e,inputs:t,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=e.makeOutput(f,\"float32\"),x=e.dataIdMap.get(n.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),C=new Uint8Array(new Int32Array(f).buffer),w=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),k=e.dataIdMap.get(h.dataId).id;return oO(x,s,a===\"NHWC\"?1:0,b,n.shape.length-1,C,w,f.length,k),h}var nO={kernelName:Yo,backendName:\"wasm\",setupFunc:U9,kernelFunc:G9};var sO;function H9(r){sO=r.wasm.cwrap(Qo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function 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ds=O();ds.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);ds.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);ds.registerFlag(\"WEBGPU_MATMUL_PROGRAM_TYPE\",()=>-1);ds.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);ds.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);ds.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);ds.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);ds.registerFlag(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\",()=>!0);ds.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\",()=>!1);ds.registerFlag(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\",()=>0);ds.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);var kg=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith(\"gen\"))return Number(this.architecture.match(/\\d+/));if(this.architecture.startsWith(\"xe\"))return 12}return 0}isIntel(){return this.vendor===\"intel\"}};var Ng=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,o=!1){let n=$3(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 a=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(a),a}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t,mappedAtCreation:o});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,o){if(this.freeBuffers.size===0)return;let n=$3(t,o);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let s=this.usedBuffers.get(n),a=s.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");s.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,o){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,o)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function $3(r,e){return`${r}_${e}`}var Tg=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,o,n){let s=A3(o),a=e*t*s,i=E3(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e,t,o,n,s){if(this.freeTextures.size===0)return;let a=E3(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=A3(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function E3(r,e,t,o){return`${r}_${e}_${t}_${o}`}function A3(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function R3(r,e){if(Math.max(...r)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let t=r.length,o=r.map(s=>`${e}[${s}]`),n=new Array(t-1);n[t-2]=o[t-1];for(let s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}var Ic=(r,e,t)=>t===\"int32\"?`atomicAdd(${r}, bitcast<i32>(${e}));`:`\n          {\n            var oldValue = 0;\n            loop {\n              let newValueF32 = bitcast<f32>(oldValue) + (${e});\n              let newValue = bitcast<i32>(newValueF32);\n              let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n              if res.exchanged {\n                break;\n              }\n              oldValue = res.old_value;\n            }\n          }`;var M3=(r,e,t,o)=>{let n={dtype:o.dtype,shape:o.shape},s=Yee(t,n,e),a=r.createShaderModule({code:s,label:e.constructor.name});return r.createComputePipeline({compute:{module:a,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"})};function Rt(r){if(r<=1)return\"i32\";if(r===2)return\"vec2<i32>\";if(r===3)return\"vec3<i32>\";if(r===4)return\"vec4<i32>\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Ao(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function Q(...r){let e;switch(r.length){case 0:e=`\n        fn main()\n      `;break;case 1:e=`\n        fn main(${r[0]} : i32)\n      `;break;default:throw Error(\"Unreachable\")}return e}function D3(r,e){let t;return t=`\n     ${Xee(e)}\n      fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n                @builtin(global_invocation_id) GlobalId : vec3<u32>,\n                @builtin(local_invocation_index) LocalIndex: u32,\n                @builtin(workgroup_id) WorkgroupId : vec3<u32>,\n                @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {\n        localId = LocalId;\n        localIndex = LocalIndex;\n        globalId = GlobalId;\n        numWorkgroups = NumWorkgroups;\n        workgroupId = WorkgroupId;\n        ${r?\"main(getGlobalIndex());\":\"main();\"};\n      }\n    `,t}function Xee(r){return`\n  @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function Yee(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(o.push(`\n\n      var<private> localId: vec3<u32>;\n      var<private> localIndex: u32;\n      var<private> globalId: vec3<u32>;\n      var<private> numWorkgroups: vec3<u32>;\n      var<private> workgroupId: vec3<u32>;\n\n      // Only used when the y/z dimension of workgroup size is 1.\n      fn getGlobalIndex() -> i32 {\n        ${B3(t)?\"  return i32(globalId.x);\":`  return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n                workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n                localIndex);\n        `}\n      }\n    `),t.isFromPixels){o.push(`\n        struct Uniform {\n          size            : i32,\n          numChannels     : i32,\n          outShapeStrides : vec2<i32>,\n        };\n\n        @group(0) @binding(0) var<storage, read_write> result: array<${vc(e.dtype,t.isVec4)}>;\n        @group(0) @binding(2) var<uniform> uniforms: Uniform;\n      `);let f=P3(t);return[F3,o.join(`\n`),O3(e.shape),t.getUserCode(),D3(f,t)].join(`\n`)}let s=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=Rt(r[h].shape.length);s+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `});let a=Rt(e.shape.length);s+=`outShape : ${a}, `;let i=e.shape.length-1,p=Rt(i);s+=`\n         outShapeStrides: ${p}, `,t.size&&(s+=\"size : i32, \"),t.uniforms&&(s+=t.uniforms),s+=\"};\",s=nte(s),o.push(s),t.atomic?o.push(`\n      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n    `):o.push(`\n      @group(0) @binding(0) var<storage, read_write> result: array<${vc(e.dtype,t.isVec4)}>;\n    `),t.variableNames.forEach((f,h)=>{o.push(`\n      @group(0) @binding(${1+h}) var<storage, read> ${f}: array<${t.variableTypes?t.variableTypes[h]:vc(r[h].dtype,t.isVec4)}>;\n        `)}),s!==\"\"&&o.push(`\n      @group(0) @binding(${1+t.variableNames.length}) var<uniform> uniforms: Uniforms;\n      `);let u=tte(e.shape,t.dispatchLayout),c=[F3,o.join(`\n`)+Qee,O3(e.shape),u,rte(e.shape.length)];t.atomic||c.push(ote(e.shape,e.dtype,t.isVec4));let l=r.map((f,h)=>ete(f,e.shape,t.variableTypes?t.variableTypes[h]===\"vec4<f32>\":t.isVec4,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=P3(t);return c.push(D3(m,t)),c.join(`\n`)}function L3(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>S.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=B3(r)?\"flatDispatch\":\"\";return n+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+e.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,n}var F3=`\n  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\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  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n  }\n  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let modulo: i32 = a % b;\n    if (sign < 0. && modulo != 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    let floatToUint: vec4<u32> = bitcast<vec4<u32>>(val);\n    return (floatToUint & vec4<u32>(0x7fffffffu)) > vec4<u32>(0x7f800000u);\n  }\n`,Qee=`\n  fn isinf(val: f32) -> bool {\n    return abs(val) == uniforms.INFINITY;\n  }\n`;function O3(r){let e=r.length;if(e<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let t=y.computeStrides(r),o=Rt(e),n=[];for(let a=0;a<e;a++)n.push(`d${a}`);if(t.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 s;return s=\"var index2 = index;\"+t.map((a,i)=>{let p=`let ${n[i]} = index2 / uniforms.outShapeStrides.${Ao(i)}`,u=i===t.length-1?`let ${n[i+1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`:`index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`;return`${p}; ${u};`}).join(\"\"),`\n    fn getCoordsFromIndex(index : i32) -> ${o} {\n      ${s}\n      return ${o}(${n.join(\",\")});\n    }\n  `}function Zee(r,e){let t=r.name,o=r.shape.length,n=Rt(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return e?`\n        fn ${s}() -> vec4<f32> {\n          return vec4<f32>(${t}[0]);\n        }\n      `:`\n      fn ${s}() ->f32 {\n        return f32(${t}[0]);\n      }\n    `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),e?`\n      fn ${s}(${i}) -> vec4<f32> {\n        return vec4<f32>(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n          ${p}) / 4]);\n      }\n      `:`\n    fn ${s}(${i}) -> f32 {\n      return f32(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n        ${p})]);\n    }\n   `}function Jee(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=Rt(p);if(y.arraysEqual(r.shape,e)&&o)return t?`\n      fn ${a}Index(globalIndex : i32) -> vec4<f32> {\n        return vec4<f32>(${n}[globalIndex]);\n      }\n\n      fn ${a}Coords(coords : ${u}) -> vec4<f32> {\n        return vec4<f32>(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n      }\n      `:`\n    fn ${a}Index(globalIndex : i32) -> f32 {\n      return f32(${n}[globalIndex]);\n    }\n\n    fn ${a}Coords(coords : ${u}) -> f32 {\n      return f32(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n    }\n    `;let c=S.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return t?`\n    fn ${a}Index(globalIndex : i32) -> vec4<f32> {\n      return get${s}();\n    }\n\n    fn ${a}Coords(coords : ${u}) -> vec4<f32> {\n      return get${s}();\n    }\n  `:`\n    fn ${a}Index(globalIndex : i32) -> f32{\n      return get${s}();\n    }\n\n    fn ${a}Coords(coords : ${u}) -> f32{\n      return get${s}();\n    }\n  `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Ao(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=Rt(i),x=r.shape.map((b,C)=>`coords.${Ao(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return t?`\n    fn ${a}Index(globalIndex : i32) -> vec4<f32> {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${m}\n      return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n    }\n\n    fn ${a}Coords(coordsIn : ${u}) -> vec4<f32> {\n      var coords = coordsIn;\n      ${m}\n      return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n    }\n  `:`\n  fn ${a}Index(globalIndex : i32) -> f32 {\n    var coords = getCoordsFromIndex(globalIndex);\n    ${m}\n    return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n  }\n\n  fn ${a}Coords(coordsIn : ${u}) -> f32 {\n    var coords = coordsIn;\n    ${m}\n    return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n  }\n`}function ete(r,e,t,o){let n=Zee(r,t);return r.shape.length<=e.length&&(n+=Jee(r,e,t,o)),n}function tte(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${Rt(s)}{\n    let globalIndex = getGlobalIndex();\n    return getCoordsFromIndex(globalIndex);\n  }\n  `;let i=\"\",p=[t,o,n];for(let m=0;m<p.length;m++){let d=p[m];if(d.length!==0)if(d.length===1)i+=`let d${d[0]} = i32(globalId[${m}]);`;else{let f=R3(d,\"uniforms.outShape\");i+=`var index${m} = i32(globalId[${m}]);`;for(let h=0;h<f.length;h++)i+=`let d${d[h]} = index${m} / ${f[h]};`,h===f.length-1?i+=`let d${d[h+1]} = index${m} - d${d[h]} * ${f[h]};`:i+=`index${m} = index${m} - d${d[h]} * ${f[h]};`}}let u=[];for(let m=0;m<a;m++)u.push(`d${m}`);let c=Rt(a),l=`fn getOutputCoords() -> ${c} {\n  ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function rte(r){let e=\"\";switch(r){case 0:case 1:e+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:e+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:e+=`\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:e+=`\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;case 5:e+=`\n        fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u;\n        }\n        `;break;case 6:e+=`\n        fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u * uniforms.outShapeStrides.u +\n              coords.v;\n        }\n        `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function B3(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function vc(r,e){return r===\"float32\"?e?\"vec4<f32>\":\"f32\":r===\"int32\"||r===\"bool\"?e?\"vec4<i32>\":\"i32\":r}function ote(r,e,t){let o=r.length,n=vc(e,t),s;if(t?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${n}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${n}(value);\n    }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${n}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${n}(value);\n    }`,o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=Rt(o);t?s+=`\n      fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:s+=`\n      fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return s}function nte(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function P3(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var hI={};Ge(hI,{GPUBytesPerElement:()=>_g,MatMulProgramType:()=>Ro,assertNotComplex:()=>fI,computeDispatch:()=>Y,computeWorkPerThreadForConv2d:()=>Vl,computeWorkgroupInfoForMatMul:()=>dI,computeWorkgroupSizeForConv2d:()=>Bl,flatDispatchLayout:()=>ae,isWebGPUSupported:()=>zl,tilesFitEvenlyIntoShape:()=>ate});var Ku=r=>{let e=1;for(let t=0;t<r.length;t++)e*=r[t];return e};function ate(r,e){if(r.length!==e.length)throw new Error(`Cannot compute whether rank ${r.length} tiles fit evenly into rank ${e.length} shape - ranks must match.`);return e.every((t,o)=>t%r[o]===0)}function Y(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Ku(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Ku(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Ku(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function dI(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function Bl(r,e,t=!1){if(t)return[8,8,1];let o=Ku(r.x.map(s=>e[s])),n=Ku(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function Vl(r,e,t=!1){if(t)return[4,4,1];let o=Ku(r.x.map(s=>e[s])),n=Ku(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function ae(r){return{x:r.map((e,t)=>t)}}function _g(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function zl(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function fI(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Ro;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Ro||(Ro={}));var ite=O().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),ute=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},Gi=class extends Zr{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!zl())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has(\"timestamp-query-inside-passes\"),this.adapterInfo=new kg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ng(this.device),this.textureManager=new Tg(this.device),this.tensorMap=new Fo(this,sr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),O().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 Gi.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if(\"texture\"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=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,o){if(o===\"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.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o}=t;if(o==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return o}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return this.convertAndCacheOnCPU(e,o);let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=S.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=y.convertBackendValuesAndArrayBuffer(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,o){let n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=_g(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.size<i)throw new Error(`GPUBuffer size(${e.buffer.size}) is smaller than tensor size(${i})!`);if((e.buffer.usage&(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))!==(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))throw new Error(\"GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!\");return e.zeroCopy!==!0&&(n=this.copyBuffer(n,i,n.usage)),a.resourceInfo={size:n.size,usage:n.usage,buffer:n},sr().makeTensorFromDataId(s,t,o,this)}readToGPU(e){let t=this.tensorMap.get(e),{values:o,dtype:n,shape:s,resourceInfo:a}=t;if(n===\"complex64\")throw new Error(\"Does not support reading buffer for complex64 dtype.\");if(a==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let i=a.size,p=this.bufferManager.acquireBuffer(i,a.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,p,0,i),this.submitQueue();let u=this.makeTensorInfo(s,n),c=sr().makeTensorFromTensorInfo(u),l=this.tensorMap.get(u.dataId);return l.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:p},{tensorRef:c,buffer:p,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn(\"This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.\");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if(\"texture\"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=_g(t.dtype)*y.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());if(t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let s=this.bufferManager.acquireUploadBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=s.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o);let i={size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(u=>{u.data.length===0&&(u.data=[1]);let c;switch(u.data.length){case 1:c=4;break;case 2:c=8;break;case 3:c=16;break;case 4:c=16;break;case 5:c=16;break;case 6:c=16;break;default:y.assert(!1,()=>`Unsupported ${u.data.length}D shape`)}(o===5||o===6)&&(c=16),c>s&&(s=c),t=Math.ceil(t/c)*c,o=u.data.length,n.push(t),t+=u.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((u,c)=>{let l=n[c];u.type===\"int32\"?new Int32Array(a,l,u.data.length).set(u.data):u.type===\"uint32\"?new Uint32Array(a,l,u.data.length).set(u.data):new Float32Array(a,l,u.data.length).set(u.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,a,0,t);let p={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(p),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=ute(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),i=t.concat(s).map(x=>x.shape);let h=\"int32\";i.map(x=>{a.push({type:h,data:x})});let g=y.computeStrides(s.shape);if(a.push({type:h,data:g}),e.size){let x=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.isVec4?x/4:x]})}}let p=t.map((h,g)=>{if(h.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(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=L3(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=M3(this.device,e,p,s),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),O().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.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,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=ite){return O().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDataPendingDisposal.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}};Gi.nextDataId=0;zl()&&Ti(\"webgpu\",async()=>{O().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let r={powerPreference:O().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has(\"timestamp-query-inside-passes\")&&(t.requiredFeatures=[\"timestamp-query-inside-passes\"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Gi(n,s)},3);var ge;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.EQUAL=5]=\"EQUAL\",r[r.GREATER=6]=\"GREATER\",r[r.GREATER_EQUAL=7]=\"GREATER_EQUAL\",r[r.INT_DIV=8]=\"INT_DIV\",r[r.LESS=9]=\"LESS\",r[r.LESS_EQUAL=10]=\"LESS_EQUAL\",r[r.LOGICAL_AND=11]=\"LOGICAL_AND\",r[r.LOGICAL_OR=12]=\"LOGICAL_OR\",r[r.MAX=13]=\"MAX\",r[r.MIN=14]=\"MIN\",r[r.MOD=15]=\"MOD\",r[r.MUL=16]=\"MUL\",r[r.NOT_EQUAL=17]=\"NOT_EQUAL\",r[r.POW=18]=\"POW\",r[r.PRELU=19]=\"PRELU\",r[r.SQUARED_DIFFERENCE=20]=\"SQUARED_DIFFERENCE\",r[r.SUB=21]=\"SUB\"})(ge||(ge={}));var V3=`\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `,$g=`\n  resultTemp = select(\n      resultTemp, vec4<f32>(valueForNaN),\n      vec4<bool>(isNaN) | isnanVec4(a) | isnanVec4(b));\n  `,pte=\"return a + b;\",cte=\"return areal * breal - aimag * bimag;\",lte=\"return areal * bimag + aimag * breal;\",mte=\"return a / b;\",dte=\"return f32(a == b);\",fte=\"return vec4<f32>(a == b);\",hte=\"return f32(a > b);\",gte=\"return vec4<f32>(a > b);\",xte=\"return f32(a >= b);\",yte=\"return vec4<f32>(a >= b);\",bte=`\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`,Cte=`\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`,Ste=\"return f32(a < b);\",wte=\"return vec4<f32>(a < b);\",Ite=\"return f32(a <= b);\",vte=\"return vec4<f32>(a <= b);\",kte=\"return f32(a >= 1.0 && b >= 1.0);\",Nte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,Tte=\"return f32(a >= 1.0 || b >= 1.0);\",_te=`return min(vec4<f32>(a >= vec4<f32>(1.0)) +\n  vec4<f32>(b >= vec4<f32>(1.0)), vec4<f32>(1.0));`,$te=`\n  ${V3}\n  if (b == 0.) {\n    return uniforms.NAN;\n  }\n  var resultTemp = a % b;\n  if ((a < 0. && b < 0.) || (a >= 0. && b > 0.)) {\n    return resultTemp;\n  } else {\n    return (resultTemp + b) % b;\n  }\n`,Ete=`\n  let isNaN = !vec4<bool>(b);\n  let valueForNaN = uniforms.NAN;\n  var resultTemp = vec4<f32>(a % b);\n  ${$g}\n\n  if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n    resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n  }\n  if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n    resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n  }\n  if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n    resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n  }\n  if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n    resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n  }\n\n  return resultTemp;\n`,Ate=\"return a * b;\",Rte=`\n  if (isnan(a) || isnan(b)) {\n    return 1.0;\n  }\n  return f32(a != b);\n`,Dte=`\n  var resultTemp = vec4<f32>(a != b);\n  let valueForNaN = 1.0;\n  ${$g}\n\n  return resultTemp;\n`,Fte=`\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`,Ote=`\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  let valueForNaN = uniforms.NAN;\n  ${$g}\n  return resultTemp;\n`,Pte=\"if (a < 0.0) { return b * a; }  return a;\",Mte=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`,Lte=\"return (a - b) * (a - b);\",Bte=\"return a - b;\";function gI(r,e,t=\"uniforms.NAN\"){let o=e?$g:V3;return e?`\n    let valueForNaN = ${t};\n    var resultTemp = vec4<f32>(${r}(a, b));\n    `+o+`\n    return resultTemp;\n  `:o+`\n    return ${r}(a, b);\n  `}function kc(r,e){switch(r){case ge.ADD:return pte;case ge.ATAN2:return gI(\"atan2\",e);case ge.COMPLEX_MULTIPLY_IMAG:return lte;case ge.COMPLEX_MULTIPLY_REAL:return cte;case ge.DIV:return mte;case ge.EQUAL:return e?fte:dte;case ge.GREATER:return e?gte:hte;case ge.GREATER_EQUAL:return e?yte:xte;case ge.INT_DIV:return e?Cte:bte;case ge.LESS:return e?wte:Ste;case ge.LESS_EQUAL:return e?vte:Ite;case ge.LOGICAL_AND:return e?Nte:kte;case ge.LOGICAL_OR:return e?_te:Tte;case ge.MAX:return gI(\"max\",e);case ge.MIN:return gI(\"min\",e);case ge.MOD:return e?Ete:$te;case ge.MUL:return Ate;case ge.NOT_EQUAL:return e?Dte:Rte;case ge.POW:return e?Ote:Fte;case ge.PRELU:return e?Mte:Pte;case ge.SQUARED_DIFFERENCE:return Lte;case ge.SUB:return Bte;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var X;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(X||(X={}));var Vte=\"return abs(a);\",zte=`\n  if (abs(a) > 1.) {\n    return uniforms.NAN;\n  }\n  return acos(a);\n`,Wte=`\n  if (a < 1.) {\n    return uniforms.NAN;\n  }\n  return acosh(a);\n`,Ute=`\n  if (abs(a) > 1.) {\n    return uniforms.NAN;\n  }\n  return asin(a);\n`,Gte=\"return asinh(a);\",Hte=`\n  if (isnan(a)) {\n    return uniforms.NAN;\n  }\n  return atan(a);\n`,Kte=`\n  if (abs(a) > 1.) {\n    return uniforms.NAN;\n  }\n  if (a == 1.) {\n    return uniforms.INFINITY;\n  }\n  if (a == -1.) {\n    return -uniforms.INFINITY;\n  }\n  return atanh(a);\n`,qte=\"return ceil(a);\",jte=\"return cos(a);\",Xte=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,Yte=\"return exp(a) - 1.0;\",Qte=\"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`,Jte=`\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  let p = ${S.ERF_P};\n  let a1 = ${S.ERF_A1};\n  let a2 = ${S.ERF_A2};\n  let a3 = ${S.ERF_A3};\n  let a4 = ${S.ERF_A4};\n  let a5 = ${S.ERF_A5};\n\n  let sign = sign(a);\n  let absA = abs(a);\n  let t = 1.0 / (1.0 + p * absA);\n  return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,ere=\"return exp(a);\",tre=\"return floor(a);\",rre=\"return f32(!isnan(a) && !isinf(a));\",ore=\"return f32(isinf(a));\",nre=\"return f32(isnan(a));\",sre=\"return a;\",are=`if (a < 0.0) { return uniforms.NAN; }\n  return log(a);`,ire=`\n  if (isnan(a)) { return a; }\n  return log(1.0 + a);\n`,ure=\"return f32(!(a >= 1.0));\",pre=\"return -a;\",cre=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",lre=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`,mre=\"return 1.0 / a;\",dre=\"return select(a, 0.0, a < 0.0);\",fre=\"return clamp(a, 0.0, 6.0);\",hre=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",gre=`\n  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));\n`,xre=\"return round(a);\",yre=\"return inverseSqrt(a);\",bre=`\n  if (a >= 0.0) {\n    return ${S.SELU_SCALE} * a;\n  } else {\n    return ${S.SELU_SCALEALPHA} * (exp(a) - 1.0);\n  }\n`,Cre=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Sre=\"return sign(a);\",wre=\"return sin(a);\",Ire=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,vre=`\n  let epsilon = 1.1920928955078125e-7;\n  let threshold = log(epsilon) + 2.0;\n\n  let too_large = a > -threshold;\n  let too_small = a < threshold;\n  let exp_a = exp(a);\n\n  if (too_large) {\n    return a;\n  } else if (too_small) {\n    return exp_a;\n  } else {\n    return log(exp_a + 1.0);\n  }\n`,kre=\"return sqrt(a);\",Nre=\"return a * a;\",Tre=`\n  if (isnan(a)) {\n    return a;\n  }\n\n  return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,_re=\"return tan(a);\",$re=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Ere=\"return f32(i32((a)));\";function ja(r,e){switch(r){case X.ABS:return Vte;case X.ACOS:return zte;case X.ACOSH:return Wte;case X.ASIN:return Ute;case X.ASINH:return Gte;case X.ATAN:return Hte;case X.ATANH:return Kte;case X.COS:return jte;case X.COSH:return Xte;case X.CEIL:return qte;case X.ELU:return e?Zte:Qte;case X.ERF:return Jte;case X.EXP:return ere;case X.EXPM1:return Yte;case X.FLOOR:return tre;case X.IS_FINITE:return rre;case X.IS_INF:return ore;case X.IS_NAN:return nre;case X.LINEAR:return sre;case X.LOG:return are;case X.LOG1P:return ire;case X.LOGICAL_NOT:return ure;case X.NEG:return pre;case X.LEAKYRELU:return e?lre:cre;case X.RECIPROCAL:return mre;case X.RELU:return e?gre:dre;case X.RELU6:return e?hre:fre;case X.ROUND:return xre;case X.RSQRT:return yre;case X.SELU:return bre;case X.SIGMOID:return Cre;case X.SIGN:return Sre;case X.SIN:return wre;case X.SINH:return Ire;case X.SOFTPLUS:return vre;case X.SQRT:return kre;case X.SQUARE:return Nre;case X.STEP:return Tre;case X.TAN:return _re;case X.TANH:return $re;case X.TO_INT:return Ere;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var kt=r=>{switch(r){case 1:return\"f32\";case 2:return\"vec2<f32>\";case 3:return\"vec3<f32>\";case 4:return\"vec4<f32>\";default:throw new Error(`${r}-component is not supported.`)}};function pr(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=ja(X.LINEAR);else if(r===\"relu\")n=ja(X.RELU,t);else if(r===\"elu\")n=ja(X.ELU,t);else if(r===\"relu6\")n=ja(X.RELU6,t);else if(r===\"prelu\")n=kc(ge.PRELU,t);else if(r===\"sigmoid\")n=ja(X.SIGMOID,t);else if(r===\"leakyrelu\")n=ja(X.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=kt(t?4:1),i=\"\";return e?i=`\n      fn activation(a : ${a}, coords : vec${o}<i32>) -> ${a} {\n        let b = getPreluActivationWeightsByOutputCoords(coords);\n        ${n}\n      }`:i=`\n      fn activation(a : ${a}, coords : vec${o}<i32>) -> ${a} {\n        ${n}\n      }`,i}function Gr(r,e){return`\n      ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n      ${e?\"value = activation(value, coords);\":\"\"}\n      `}function xI(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n      ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n    `,i=e?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n  fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${kt(s)} {\n    var value = ${kt(s)}(0.0);\n    let col = colIn * ${s};\n    ${t&&n?a:`\n    ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n    {\n      ${a}\n    }\n    `}\n    return value;\n  }\n\n  fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${kt(s)} {\n    let col = colIn * ${s};\n    var value = ${kt(s)}(0.0);\n    ${i}\n    return value;\n  }\n  `}function Wl(r,e,t,o,n=!1,s=!1,a=!1,i=1){return`\n  ${xI(t,o,n,s,a,i)}\n  fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(i)}) {\n    let col = colIn * ${i};\n    ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n    {\n      var value = valueIn;\n      let coords = vec3<i32>(batch, row, col);\n      ${Gr(r,e)}\n      setOutputAtCoords(coords[0], coords[1], coords[2], value);\n    }\n  }\n  `}var Are=(r,e)=>r?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n          kStart + inputRow,\n          globalRowStart / ${e} + inputCol);\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n          globalRow + innerRow,\n          kStart / ${e} + inputCol);\n        `,Rre=(r,e,t)=>r?`\n        let ACached0 = mm_Asub[k * ${e}][localRow];\n        let ACached1 = mm_Asub[k * ${e} + 1][localRow];\n        let ACached2 = mm_Asub[k * ${e} + 2][localRow];\n        ${e===3?\"\":`let ACached3 = mm_Asub[k * ${e} + 3][localRow];`}\n        for (var i = 0; i < ${t}; i++) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${e===3?\"\":\"acc[i] = BCached3 * ACached3[i] + acc[i];\"}\n        }`:`\n        for (var i = 0; i < ${t}; i++) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${e===3?\"\":\"acc[i] = BCached3 * ACached.w + acc[i];\"}\n        }`;function qu(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=e[1]*r[1],u=e[0]*r[0],c=t?p:o,l=t?o:p,m=c/e[0],d=o/e[1],f=r[1];return y.assert((t&&m===4&&r[1]===4||!t&&(m===3||m===4))&&c%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${m} and workPerThread[1] ${r[1]} must be 4.\n          Otherwise, innerElementSize ${m} must be 3 or 4.\n      tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n  var<workgroup> mm_Asub : array<array<vec${m}<f32>, ${c/m}>, ${l}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${u/r[0]}>, ${o}>;\n\n  ${Q()} {\n    let localRow = i32(localId.y);\n    let tileRow = ${a?\"0\":`localRow * ${f}`};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${a?\"0\":`i32(globalId.y) * ${f}`};\n    let globalCol = i32(globalId.x);\n    let batch = ${n?\"0\":\"i32(globalId.z)\"};\n    let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n    let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n    let globalRowStart = i32(workgroupId.y) * ${p};\n\n    let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n    var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n    var acc: array<vec4<f32>, ${f}>;\n\n    // Loop over shared dimension.\n    let tileRowB = localRow * ${d};\n    for (var t = 0; t < numTiles; t++) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            ${Are(t,m)}\n        }\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n        }\n        kStart = kStart + ${o};\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${o/m}; k++) {\n            let BCached0 = mm_Bsub[k * ${m}][tileCol];\n            let BCached1 = mm_Bsub[k * ${m} + 1][tileCol];\n            let BCached2 = mm_Bsub[k * ${m} + 2][tileCol];\n            ${m===3?\"\":`let BCached3 = mm_Bsub[k * ${m} + 3][tileCol];`}\n\n            ${Rre(t,m,f)}\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n        mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n    }\n  }`}var z3=r=>r?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n          kStart + inputRow,\n          globalRowStart + inputCol);\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n          globalRowStart + inputRow,\n          kStart + inputCol);\n        `,Dre=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function ju(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?`\n      let localRow = i32(localId.y);\n      let localCol = i32(localId.x);\n      let globalRowStart = i32(workgroupId.y) * ${p};\n      let globalColStart = i32(workgroupId.x) * ${u};\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t++) {\n        // Load one tile of A into local memory.\n        for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {\n          for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {\n            ${z3(t)}\n          }\n        }\n        // Load one tile of B into local memory.\n        for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n              for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n            mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n              kStart + inputRow,\n              globalColStart + inputCol);\n          }\n        }\n        kStart = kStart + ${o};\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        var BCached : array<f32, ${g}>;\n        for (var k = 0; k < ${o}; k++) {\n          for (var inner = 0; inner < ${g}; inner++) {\n            BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n          }\n          for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n            let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n            for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n                  ACached * BCached[innerCol];\n            }\n          }\n        }\n        workgroupBarrier();\n      }\n      for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n        let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n        for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n          let gCol = globalColStart + localCol + innerCol * ${e[0]};\n          mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n        }\n      }\n      `:`\n  let tileRow = i32(localId.y) * ${h};\n  let tileCol = i32(localId.x) * ${g};\n\n  let globalRow = i32(globalId.y) * ${h};\n  let globalCol = i32(globalId.x) * ${g};\n  let globalRowStart = i32(workgroupId.y) * ${p};\n\n  let tileRowA = i32(localId.y) * ${m};\n  let tileColA = i32(localId.x) * ${d};\n  let tileRowB = i32(localId.y) * ${f};\n  // Loop over shared dimension.\n  for (var t = 0; t < numTiles; t++) {\n    // Load one tile of A into local memory.\n    for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n      for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n        let inputRow = tileRowA + innerRow;\n        let inputCol = tileColA + innerCol;\n        ${z3(t)}\n      }\n    }\n\n    // Load one tile of B into local memory.\n    for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n      for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n        let inputRow = tileRowB + innerRow;\n        let inputCol = tileCol + innerCol;\n        mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n          kStart + inputRow,\n          globalCol + innerCol);\n      }\n    }\n    kStart = kStart + ${o};\n    workgroupBarrier();\n\n    // Compute acc values for a single thread.\n    var BCached : array<f32, ${g}>;\n    for (var k = 0; k < ${o}; k++) {\n      for (var inner = 0; inner < ${g}; inner++) {\n        BCached[inner] = mm_Bsub[k][tileCol + inner];\n      }\n\n      for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n        ${Dre(t)}\n        for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n          acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n        }\n      }\n    }\n\n    workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n    for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n      mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n          acc[innerRow][innerCol]);\n    }\n  }\n  `;return`\n    var<workgroup> mm_Asub : array<array<f32, ${c}>, ${l}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${u}>, ${o}>;\n\n    ${Q()} {\n      let batch = ${n?\"0\":\"i32(globalId.z)\"};\n      let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n      let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n      let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n      var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n      var acc : array<array<f32, ${g}>, ${h}>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n        for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n      ${x}\n    }\n  `}var Fre=r=>r?`\n      mm_readA(batchA, colA, globalRow),\n      mm_readA(batchA, colA + 1, globalRow),\n      mm_readA(batchA, colA + 2, globalRow),\n      mm_readA(batchA, colA + 3, globalRow)\n  `:`\n      mm_readA(batchA, globalRow, colA),\n      mm_readA(batchA, globalRow, colA + 1),\n      mm_readA(batchA, globalRow, colA + 2),\n      mm_readA(batchA, globalRow, colA + 3)\n  `;function Ore(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return`\n    var<workgroup> mm_Asub : array<vec4<f32>, ${r[0]}>;\n\n    ${Q()} {\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) / ${t} + 1;\n      let batch = i32(globalId.z);\n      let batchA = batch % uniforms.aShape[0];\n      let batchB = batch % uniforms.bShape[0];\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++) {\n        // Load one tile of A into local memory.\n        let colA = t * ${t} + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(${Fre(e)});\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${t/4}; k++) {\n          let rowB = t * ${t} + k * 4;\n          let BCached = vec4<f32>(mm_readB(batchB, rowB, globalCol),\n                              mm_readB(batchB, rowB + 1, globalCol),\n                              mm_readB(batchB, rowB + 2, globalCol),\n                              mm_readB(batchB, rowB + 3, globalCol));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      mm_write(batch, globalRow, globalCol, acc);\n    }\n  `}var Eg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=dI(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n      ${pr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n      ${Wl(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n      ${this.isVec4?qu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA,!0):this.isVectorA?Ore(this.workgroupSize,this.transposeA):ju(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n    `}};function Pre(r){return`\n    var<workgroup> sumValues : array<f32, ${r}>;\n    ${Q()} {\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let batchA = batch % uniforms.aShape[0];\n      let batchB = batch % uniforms.bShape[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 + ${r}) {\n        let dataA = mm_readA(batchA, row, k);\n        let dataB = mm_readB(batchB, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = ${r/2}u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var Ag=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return`\n      ${pr(this.activation,this.hasPreluActivationWeights)}\n      ${Wl(this.addBias,this.activation,this.transposeA,this.transposeB)}\n      ${Pre(this.workgroupSize[0])}\n    `}};function Mre(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n  var<workgroup> mm_Asub : array<array<f32, ${o}>, ${e}>;\n  var<workgroup> mm_Bsub : array<array<f32, ${t}>, ${o}>;\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  // Read data from global memory to registers firstly, then store them into\n  // shared memory, so it is instruction-Level parallelism for arithmetic\n  // operations and others handle IO operations between barrier api, makes ALU\n  // and load/store units work simultaneously, could improves the performance.\n  ${Q()} {\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    let batch = i32(globalId.z);\n    let batchA = batch % uniforms.aShape[0];\n    let batchB = batch % uniforms.bShape[0];\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = 0;\n    var regA = mm_readA(batchA, globalRow, globalColA);\n    var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n    var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n    globalColA = globalColA + ${o};\n    globalRowB = globalRowB + ${o};\n\n    for (var t = 0; t < numTiles; t = t + 1) {\n      mm_Asub[tileRow][tileCol] = regA;\n      mm_Bsub[2 * tileRow][tileCol] = regB0;\n      mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n      workgroupBarrier();\n\n      regA = mm_readA(batchA, globalRow, globalColA);\n      regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n      regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n      globalColA = globalColA + ${o};\n      globalRowB = globalRowB + ${o};\n\n      for (var k = 0; k < ${o}; k = k + 1) {\n        acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n      }\n      workgroupBarrier();\n    }\n\n    mm_write(batch, globalRow, globalCol, acc);\n  }\n  `}var Rg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n      ${pr(this.activation,this.hasPreluActivationWeights)}\n      ${Wl(this.addBias,this.activation,this.transposeA,this.transposeB)}\n      ${Mre(this.workgroupSize)}\n    `}};var Dg=class{constructor(e,t,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=Y(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=this.isVec4?4:1;return`\n      ${xI(!1,this.transposeB,!1,!1,!1,e)}\n      fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(e)}) {\n        let col = colIn * ${e};\n        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n          let coords = vec3<i32>(batch, row, col);\n          let flatIndex = getOutputIndexFromCoords(coords);\n          // The problem is that we should initialize output to zero before using.\n          // Otherwise, the original value will be added to the result.\n          for (var i = 0; i < ${e}; i = i + 1) {\n            ${Ic(\"&result[flatIndex + i]\",`${e>1?\"value[i]\":\"value\"}`,\"float32\")}\n          }\n        }\n      }\n      ${this.isVec4?qu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):ju(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n    `}},Fg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n    ${pr(this.activation,this.hasPreluActivationWeights)}\n    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        var value = getXByOutputIndex(index);\n        ${Gr(this.addBias,this.activation)}\n        setOutputAtIndex(index, value);\n      }\n    }\n    `}};var Og=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function fr(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Og(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var W3={kernelName:ws,backendName:\"webgpu\",kernelFunc:fr};function me(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var U3={kernelName:_s,backendName:\"webgpu\",kernelFunc:me};function Xu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=yr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=me({inputs:{x:r},backend:n,attrs:{shape:k}}),A=me({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[E,A],D=Math.max(x,b),P=[E,A],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,V,z=[D,d,f],U=O().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let H=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),q=H>0?H:n.thresholdToIncreaseWorkgroups,Z=D*Math.ceil(d/32)*Math.ceil(f/32);Z<=q||d<=8&&Z<=q*2?D*d*f<=128?U=Ro.MatMulReduceProgram:D===1&&m>=2e3?U=Ro.MatMulSplitKProgram:U=Ro.MatMulSmallOutputSizeProgram:U=Ro.MatMulPackedProgram}switch(U){case Ro.MatMulReduceProgram:L=new Ag(z,t,o,s,p,a);break;case Ro.MatMulSplitKProgram:{if(V=fr({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Dg(z,m,t,o),s||p){V=n.runWebGPUProgram(L,P,r.dtype,M,V);let q=new Fg(V.shape,s,p,a),Z=null,ee=[V];s&&ee.push(s),a&&ee.push(a),p===\"leakyrelu\"&&(Z=[{type:\"float32\",data:[i]}],q.uniforms+=\" alpha : f32,\");let oe=n.runWebGPUProgram(q,ee,V.dtype,Z);R.push(V);let J=me({inputs:{x:oe},backend:n,attrs:{shape:w}});R.push(oe);for(let te of R)n.disposeData(te.dataId);return J}break}case Ro.MatMulSmallOutputSizeProgram:L=new Rg(k,_,z,t,o,s,p,a);break;case Ro.MatMulPackedProgram:let H=n.adapterInfo.isIntel();L=new Eg(k,z,t,o,s,p,a,H);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&P.push(s),a&&P.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),V=n.runWebGPUProgram(L,P,r.dtype,M,V);let K=me({inputs:{x:V},backend:n,attrs:{shape:w}});R.push(V);for(let H of R)n.disposeData(H.dataId);return K}function Lre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Xu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var G3={kernelName:ho,backendName:\"webgpu\",kernelFunc:Lre};var Ul=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${kc(this.op,!1)}\n      }\n\n      ${Q(\"index\")} {\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}};var Yu=class{constructor(e,t,o){this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ae(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type=\"shared\",this.workgroupSize=[256,1,1],this.workPerThread=1):(y.arraysEqual(t,o)&&y.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type=\"vec4\",this.workPerThread=4):(this.isVec4=!1,this.type=\"plain\",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?\"vec4<f32>\":\"f32\",o=`\n    fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n      let isNaN = false;\n      {\n        ${kc(this.op,this.isVec4)}\n      }\n    };\n    `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n          let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n          let b = getBByOutputIndex(index);`;e=`\n        ${o}\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Q(\"index\")} {\n          // Fill in the shared memory buffer.\n          let localIndex = i32(localId.x);\n          if(localIndex < ${this.lastDimensionSize}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          if(index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            ${s}\n            setOutputAtIndex(index, binaryOperation(a, b));\n          }\n        }\n        `}else e=`\n       ${o}\n       ${Q(\"index\")} {\n         if (index < uniforms.size) {\n           let a = getAByOutputIndex(index);\n           let b = getBByOutputIndex(index);\n           setOutputAtIndex(index, binaryOperation(a, b));\n         }\n       }\n       `;return e}};function Dt(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var H3={kernelName:mo,backendName:\"webgpu\",kernelFunc:Dt};function po(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var K3={kernelName:ri,backendName:\"webgpu\",kernelFunc:po};var Hr=class{constructor(e,t,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${ja(this.op,!1)}\n      }\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function xe({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Hr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function Ze({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==ge.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},w={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Yu(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,w],dt(x.dtype,b.dtype))});else{let g=new Ul(ge.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new Ul(ge.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{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},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=po({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?S.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Yu(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:q3,castImpl:j3,ceilImpl:X3,concatImpl:Y3,equalImpl:Q3,expImpl:Z3,expm1Impl:J3,floorImpl:eM,gatherNdImpl:tM,gatherV2Impl:rM,greaterEqualImpl:oM,greaterImpl:nM,lessEqualImpl:sM,lessImpl:aM,logImpl:iM,maxImpl:uM,maximumImpl:pM,minimumImpl:cM,multiplyImpl:lM,negImpl:mM,notEqualImpl:dM,prodImpl:fM,rangeImpl:hM,rsqrtImpl:gM,scatterImpl:xM,simpleAbsImpl:yM,sliceImpl:bM,stridedSliceImpl:CM,stringNGramsImpl:SM,subImpl:wM,tileImpl:IM,topKImpl:vM,transposeImpl:kM,uniqueImpl:iTt}=Zp;var Bre=xe({opType:X.ABS,cpuKernelImpl:yM}),NM={kernelName:ys,backendName:\"webgpu\",kernelFunc:Bre};var Vre=xe({opType:X.ACOS}),TM={kernelName:aa,backendName:\"webgpu\",kernelFunc:Vre};var zre=xe({opType:X.ACOSH}),_M={kernelName:ia,backendName:\"webgpu\",kernelFunc:zre};var Wre=Ze({opType:ge.ADD,cpuKernelImpl:q3,supportsComplex:!0}),$M={kernelName:eo,backendName:\"webgpu\",kernelFunc:Wre};var Pg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n      ${Q(\"index\")} {\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 Ure(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Pg(s);return t.runWebGPUProgram(a,o,n)}var EM={kernelName:Po,backendName:\"webgpu\",kernelFunc:Ure};var Mg=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n<o.length;n++)o[n]=e[t[n]];this.outputShape=o,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){y.assert(this.workgroupSize[0]===this.workgroupSize[1],()=>`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`\n      var<workgroup> tile : array<array<f32, ${this.workgroupSize[0]+1}>, ${this.workgroupSize[0]}>;\n      ${Q()} {\n        var x = i32(workgroupId.x) * ${e} + i32(localId.x);\n        var y = i32(workgroupId.y) * ${e} + 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] = f32(A[y * width + x]);\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * ${e} + i32(localId.x);\n        y = i32(workgroupId.x) * ${e} + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}};var Lg=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n<o.length;n++)o[n]=e[t[n]];this.outputShape=o,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Rt(this.outputShape.length),t=Gre(this.newDim);return`\n      ${Q(\"index\")} {\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function Gre(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o<r.length;o++)t[r[o]]=`resRC.${Ao(o)}`;return t.join()}function Nr(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,p=new Array(i);for(let c=0;c<p.length;c++)p[c]=n.shape[s[c]];if(t.shouldExecuteOnCPU([n])){let l=a.tensorMap.get(n.dataId).values,m=kM(l,n.shape,n.dtype,s,p);return t.makeTensorInfo(p,n.dtype,m)}if(n.shape.length===2&&y.arraysEqual(s,[1,0])){let c=new Mg(n.shape,s);return a.runWebGPUProgram(c,[n],n.dtype)}let u=new Lg(n.shape,s);return a.runWebGPUProgram(u,[n],n.dtype)}var AM={kernelName:ro,backendName:\"webgpu\",kernelFunc:Nr};var Bg=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[o]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=o.length===0?[1]:o,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\",o=this.workgroupSize[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[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");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, ${o}>;\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       ${Q(\"index\")} {\n         let outputIndex = index / ${o};\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + ${o}) {\n           let candidate = f32(x[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), ${o}u);\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          ${n}\n        }\n       }\n     `}};function Kr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=S.getAxesPermutation(p,s),c=r;u!=null&&(c=Nr({inputs:{x:r},attrs:{perm:u},backend:n}),p=S.getInnerMostAxes(p.length,s),a.push(c)),S.assertAxesAreInnerMostDims(o,p,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=S.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=uM(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=fM(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=o===\"mean\"?\"float32\":Ta(r.dtype),w=[{type:\"int32\",data:[h]}],k=new Bg(b,o),_=n.runWebGPUProgram(k,[c],C,w);a.push(_),f=me({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function Hre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,\"all\",t)}var RM={kernelName:Mo,backendName:\"webgpu\",kernelFunc:Hre};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,\"any\",t)}var DM={kernelName:Lo,backendName:\"webgpu\",kernelFunc:Kre};var Nc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=S.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ae(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type=\"plain\",this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=Y(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Ao(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s<this.outputShape.length;s++)n+=`outputCoords.${Ao(s)},`;return n};return this.type===\"shared\"?`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${`\n      var<workgroup> xBestIndices : array<i32, ${e}>;\n      var<workgroup> xBestValues : array<f32, ${e}>;\n    `}\n\n      ${Q(\"index\")} {\n        let outputIndex = index / ${e};\n        let reduceLength = ${t()};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n            k = k + ${e}) {\n          let candidate = getX(${o()} 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(reduceLength), ${e}u);\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    `:`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let outputCoords = getCoordsFromIndex(index);\n          var bestIndex = 0;\n          var bestValue = getX(${o()} 0);\n          let reduceLength = ${t()};\n          for (var i = 1; i < reduceLength; i++) {\n            let candidate = getX(${o()} i);\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              bestIndex = i;\n            }\n          }\n          setOutputAtIndexI32(index, bestIndex);\n        }\n      }\n      `}};function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new Nc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var FM={kernelName:Bo,backendName:\"webgpu\",kernelFunc:qre};function jre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new Nc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var OM={kernelName:ei,backendName:\"webgpu\",kernelFunc:jre};var Xre=xe({opType:X.ASIN}),PM={kernelName:ua,backendName:\"webgpu\",kernelFunc:Xre};var Yre=xe({opType:X.ASINH}),MM={kernelName:pa,backendName:\"webgpu\",kernelFunc:Yre};var Qre=xe({opType:X.ATAN}),LM={kernelName:ca,backendName:\"webgpu\",kernelFunc:Qre};var Zre=Ze({opType:ge.ATAN2}),BM={kernelName:ma,backendName:\"webgpu\",kernelFunc:Zre};var Jre=xe({opType:X.ATANH}),VM={kernelName:la,backendName:\"webgpu\",kernelFunc:Jre};var Gl=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=ae(this.outputShape),this.dispatch=Y(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 / max(count, 1.0)\"),`\n      ${Q(\"index\")} {\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    `}};var Vg=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Q(\"index\")} {\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 Hl(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return Kr(n,s,a,\"max\",t)}var zM={kernelName:yn,backendName:\"webgpu\",kernelFunc:Hl};function yI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return Kr(n,a,s,\"mean\",t)}var WM={kernelName:Sn,backendName:\"webgpu\",kernelFunc:yI};function zg(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Dt({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=me({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=yI({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=Hl({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=me({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new Vg(e):(t===\"avg\"?n=new Gl(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Gl(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function eoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return zg(n,c,\"avg\",t)}var UM={kernelName:Vo,backendName:\"webgpu\",kernelFunc:eoe};var Wg=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`stride : vec2<i32>, pads : vec2<i32>, dilation : vec2<i32>, filterDims : vec2<i32>,\n       outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avg_pool2d_backprop\"}getUserCode(){return`\n      ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d = coords[3];\n\n        let dyRCCorner = vec2<i32>(coords.yz) - uniforms.pads;\n        let dyRCorner = dyRCCorner.x;\n        let 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        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilation[0]) {\n          let dyR = f32(dyRCorner + wR) / f32(uniforms.stride[0]);\n\n          if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n            continue;\n          }\n          let idyR = i32(dyR);\n\n          for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilation[1]) {\n            let dyC = f32(dyCCorner + wC) / f32(uniforms.stride[1]);\n\n            if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n              continue;\n            }\n            let idyC = i32(dyC);\n\n            let dyValue = getDy(batch, idyR, idyC, d);\n\n            dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n    `}};function toe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;fI([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new Wg(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var GM={kernelName:mp,backendName:\"webgpu\",kernelFunc:toe};function roe(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Xu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var HM={kernelName:zo,backendName:\"webgpu\",kernelFunc:roe};var Ug=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Rt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Rt(this.rank),t=ooe(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${bI[a]} = uniforms.start.${Ao(a)} + coords.${bI[a]};`),`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${o.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},bI=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function ooe(r){if(r===1)return\"sourceLoc\";if(r<=6)return bI.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function fs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=bM(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Ug(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var KM={kernelName:Es,backendName:\"webgpu\",kernelFunc:fs};var noe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=me({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=me({inputs:{x:h},backend:t,attrs:{shape:c}}),x=fs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},qM={kernelName:bs,backendName:\"webgpu\",kernelFunc:noe};var soe=`\n  fn bincount_write(index: i32, value: f32) {\n    ${Ic(\"&result[index]\",\"value\",\"float32\")}\n  }\n`,aoe=`\n  fn bincount_write(index: i32, value: f32) {\n    atomicStore(&result[index], bitcast<i32>(value));\n  }\n`,Tc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n    ${this.binaryOutput?aoe:soe}\n  ${Q(\"index\")} {\n    ${this.rank===1?`if (index < uniforms.xShape) {\n      let indexVal = i32(getX(index));\n      if (indexVal < uniforms.binCountSize) {\n        let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n        bincount_write(indexVal, value);\n      }\n    }`:`let coord = getCoordsFromIndex(index);\n    if (coordsInBounds2D(coord, uniforms.xShape)) {\n      let indexVal = i32(getX(coord[0], coord[1]));\n      if (indexVal < uniforms.binCountSize) {\n        let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n        bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n      }\n    }`}\n  }\n  `}};function ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=fr({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Tc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var jM={kernelName:ti,backendName:\"webgpu\",kernelFunc:ioe};var CI=Ze({opType:ge.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:dM}),XM={kernelName:Nn,backendName:\"webgpu\",kernelFunc:CI};function Xa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var YM={kernelName:di,backendName:\"webgpu\",kernelFunc:Xa};function QM(r,e){let t=new Hr(r.shape,X.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function SI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:t});let a=Br(n.shape),i=SI({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=po({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=Xa({inputs:{input:n},backend:t}),i=SI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=j3(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return QM(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=CI({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var ZM={kernelName:co,backendName:\"webgpu\",kernelFunc:SI};var uoe=xe({opType:X.CEIL,cpuKernelImpl:X3}),JM={kernelName:Wo,backendName:\"webgpu\",kernelFunc:uoe};var Gg=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue = clamp(\n              value, vec4<f32>(uniforms.minVal), vec4<f32>(uniforms.maxVal));\n          clampedValue = select(clampedValue, value, isnanVec4(value));\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}};var Hg=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function poe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new Gg(n.shape):i=new Hg(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var eL={kernelName:lo,backendName:\"webgpu\",kernelFunc:poe};var Kg=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(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${t} : i32,`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;s<this.offsetLength;s++)e.push(`else if (yC < uniforms.offset${[s]}){ setOutputAtCoords(coords.x, coords.y, getT${s}(yR, yC - uniforms.offset${s-1})); }`);let o=this.offsetLength,n=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${o}(yR, yC - uniforms.offset${n})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Q(\"index\")} {\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 Qu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var tL={kernelName:ci,backendName:\"webgpu\",kernelFunc:Qu};function _c(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let f=r.map(C=>Xa({inputs:{input:C},backend:t})),h=r.map(C=>Qu({inputs:{input:C},backend:t})),g=_c(f,e,t),x=_c(h,e,t),b=po({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(e))];return me({inputs:{x:k},backend:t,attrs:{shape:E}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=S.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=Y3(h,g,o,x),C=S.computeOutShape(r.map(k=>k.shape),e),w=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),w}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;g<r.length;g+=s){let x=r.slice(g,g+s);f.push(_c(x,e,t))}let h=_c(f,e,t);for(let g of f)t.disposeData(g.dataId);return h}let{tensors2D:a,outShape:i}=coe(r,e,t),p=a.map(f=>f.shape),u=new Kg(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;f<l.length;f++)l[f]=l[f-1]+p[f][1],c.push({type:\"int32\",data:[l[f]]})}let m=t.runWebGPUProgram(u,a,a[0].dtype,c);a.forEach(f=>t.disposeData(f.dataId));let d=me({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function coe(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>me({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function wI(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):_c(p,s,t)}var rL={kernelName:Cs,backendName:\"webgpu\",kernelFunc:wI};function loe(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=R=>{switch(R){case 1:return\"resData = x[xIndex];\";case 3:return\"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},l=R=>{switch(R){case 1:return\"return W[row * uniforms.wShape[3] + colIn];\";case 4:return\"return W[row * uniforms.wShape[3] / 4 + colIn];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=r?`\n      let coord = vec4<i32>(batch, xRow, xCol, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, xRow, xCol);\n      `,d=r?`\n      let coords = vec4<i32>(\n        batch,\n        row / outWidth,\n        row % outWidth,\n        col);\n      `:`\n      let coords = vec4<i32>(\n        batch,\n        row,\n        col / outWidth,\n        col % outWidth);\n      `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n      let inChannels = uniforms.wShape[2];\n      let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n      let outRow = ${g} / outWidth;\n      let outCol = ${g} % outWidth;\n\n      let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n      let WCol = ${x} / inChannels % uniforms.filterDims[1];\n      let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n      let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n      let xCh = ${x} % inChannels;\n      var resData = ${kt(i)}(0.0);\n      // The bounds checking is always needed since we use it to pad zero for\n      // the 'same' padding type.\n      if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n        ${m}\n        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n        ${c(i)}\n      }\n      return resData;`,C=r?e&&o?`\n      let col = colIn * ${i};\n      ${b}`:`\n      let col = colIn * ${i};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${b}\n      }\n      return ${kt(i)}(0.0);`:o&&t?`\n      let col = colIn * ${i};\n      ${b}`:`\n      let col = colIn * ${i};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${b}\n      }\n      return ${kt(i)}(0.0);`,w=`${l(p)}`,k=kt(u),_=r?kt(i):kt(p),E=r?kt(p):kt(i);return`\n      ${pr(s,a,u===4,4)}\n      fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {\n        ${r?C:w}\n      }\n\n      fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} {\n        ${r?w:C}\n      }\n\n      fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {\n        let col = colIn * ${u};\n        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n        {\n        var value = valueIn;\n        let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n        ${d}\n        ${Gr(n,s)}\n        setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }`}var qg=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Bl(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Vl(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=[\"f32\",\"vec4<f32>\"]):(this.innerElementSize=4,this.variableTypes=[\"vec4<f32>\",\"vec4<f32>\"]),s&&(this.variableNames.push(\"bias\"),this.variableTypes.push(\"vec4<f32>\")),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableTypes.push(\"vec4<f32>\"))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?qu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):ju(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n    ${loe(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n    ${e}\n  `}};var jg=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n       ${pr(this.activation,this.hasPreluActivationWeights,!1,4)}\n       fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n         let coords = vec4<i32>(batch, row, col, chan);\n         if (coordsInBounds4D(coords, uniforms.xShape)) {\n           return  getX(batch, row, col, chan);\n         } else {\n          return 0.0;\n         }\n       }\n       fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n         let coords = vec4<i32>(row, col, xChannel, outChannel);\n         if(coordsInBounds4D(coords, uniforms.wShape)) {\n           return getW(row, col, xChannel, outChannel);\n          } else {\n            return 0.0;\n          }\n       }\n       fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n         let coords = ${this.isChannelsLast?\"vec4<i32>(batch, row, col, chan);\":\"vec4<i32>(batch, chan, row, col);\"}\n         if (coordsInBounds4D(coords, uniforms.outShape)) {\n           var value = valueIn;\n           ${Gr(this.addBias,this.activation)}\n           setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n         }\n       }\n       ${Q(\"index\")} {\n         let coords = getOutputCoords();\n         let batch = coords[0];\n         let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n         let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n         let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n         var acc : f32 = 0.0;\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             let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n             let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n             for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n               ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n               let f = readFilt(row, col, xChannel, outChannel);\n               acc = acc + v * f;\n             }\n           }\n         }\n         writeResult(batch, outRow, outCol, outChannel, acc);\n       }\n     `}};var Xg=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,\n       inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n    ${Q(\"index\")} {\n      let coords = getCoordsFromIndex(index);\n      if(index < uniforms.size) {\n        let batch = coords[0];\n        let row = ${o};\n        let col = ${n};\n        let offsetY = (row / uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0];\n        let xRow = offsetY + uniforms.dilation[0] * (col / uniforms.itemsPerBlockRow);\n        var value = 0.0;\n        if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n          let offsetX = (row % uniforms.outWidth) * uniforms.stride[1] -\n              uniforms.pad[1];\n          let xCol = offsetX + uniforms.dilation[1] * ((col %\n              uniforms.itemsPerBlockRow) / uniforms.inChannels);\n          let ch = col % uniforms.inChannels;\n          if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n            value = ${s};\n          }\n        }\n        setOutputAtIndex(index, value);\n      }\n    }\n   `}};function Yg(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function moe({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=me({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=me({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=me({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=me({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Yg(s.shape,p);x!=null&&(s=me({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Yg(n.shape,p);x!=null&&(n=me({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Xu({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=me({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function doe({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",w=p*u*c,k=h*f,_=C?[t.batchSize,k,w]:[t.batchSize,w,k],E=new Xg(_,C),A=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],R=o.runWebGPUProgram(E,[r],r.dtype,A),D=[];D.push(R);let P=me({inputs:{x:e},backend:o,attrs:{shape:[1,w,-1]}});if(D.push(P),s!=null){let U=Yg(s.shape,C);U!=null&&(s=me({inputs:{x:s},backend:o,attrs:{shape:U}}),D.push(s))}if(n!=null){let U=Yg(n.shape,C);U!=null&&(n=me({inputs:{x:n},backend:o,attrs:{shape:U}}),D.push(n))}let V=Xu({a:C?R:P,b:C?P:R,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=me({inputs:{x:V},backend:o,attrs:{shape:t.outShape}});D.push(V);for(let U of D)o.disposeData(U.dataId);return z}function Qg({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return moe({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(O().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return doe({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new jg(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,E=c?t.outChannels:t.outHeight*t.outWidth,A=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[E]},{type:\"int32\",data:[A]});let R=o.adapterInfo.isIntel();g=new qg(t,_,E,A,p,i,u,R)}let C=[],w=[r,e];p&&(!c&&n.shape.length===1&&(n=me({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),w.push(n)),u&&(!c&&s.shape.length===1&&(s=me({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),w.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,w,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function foe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return Qg({x:n,filter:s,convInfo:m,backend:o})}var oL={kernelName:Uo,backendName:\"webgpu\",kernelFunc:foe};var Zg=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=ae(this.outputShape),this.dispatch=Y(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,o=this.isChannelsLast?3:1;return`\n    ${Q(\"index\")} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${o}];\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 = i32(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 = i32(dyC);\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}},Jg=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pad : vec2<i32>, stride : vec2<i32>, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n    ${Q(\"index\")} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let wR = coords[0];\n        let wC = coords[1];\n        let d1 = coords[2];\n        let d2 = coords[3];\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n          for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n            let xR = wR + yR * uniforms.stride[0] - uniforms.pad[0];\n            if (xR < 0 || xR >= uniforms.inHeight) {\n              continue;\n            }\n\n            for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n              let xC = wC + yC * uniforms.stride[1] - uniforms.pad[1];\n\n              if (xC < 0 || xC >= uniforms.inWidth) {\n                continue;\n              }\n\n              if (${this.isChannelsLast}) {\n                let dyValue = getDy(b, yR, yC, d2);\n                let xValue = getX(b, xR, xC, d1);\n                dotProd = dotProd + xValue * dyValue;\n              } else {\n                let dyValue = getDy(b, d2, yR, yC);\n                let xValue = getX(b, d1, xR, xC);\n                dotProd = dotProd + xValue * dyValue;\n              }\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function hoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Jg(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var nL={kernelName:oi,backendName:\"webgpu\",kernelFunc:hoe};function goe(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n            let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n            let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n            let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n            return vec4<f32>(v0, v1, v2, v3);\n            `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`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 ${kt(r)}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n        return ${kt(r)}(0.0);\n      }\n      let coord = vec4<i32>(\n          batch,\n          i32(xR),\n          i32(xC),\n          col % uniforms.outBackprop[3]);\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n      }\n      return ${kt(r)}(0.0);`;return`\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n    let col = colIn * ${r};\n    ${o}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n    let col = colIn * ${r};\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 rowInner = row % uniforms.outBackprop[3];\n      let coord = vec4<i32>(coordX, coordY, col, rowInner);\n      ${e(r)}\n    }\n    return ${kt(r)}(0.0);\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${kt(r)}) {\n    let col = colIn * ${r};\n    if (row < uniforms.dimAOuter && (col + ${r-1}) < uniforms.dimBOuter) {\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n    }\n  }`}var ex=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,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Bl(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Vl(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=[\"vec4<f32>\",\"f32\"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?qu(this.elementsPerThread,this.workgroupSize):ju(this.elementsPerThread,this.workgroupSize);return`\n    ${goe(this.isVec4?4:1)}\n    ${e}\n    `}};function xoe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.filterHeight<=2&&m.filterWidth<=2&&m.outChannels<=16&&m.inChannels===1)f=new Zg(m);else{f=new ex(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var sL={kernelName:Go,backendName:\"webgpu\",kernelFunc:xoe};var yoe=xe({opType:X.COS}),aL={kernelName:Ho,backendName:\"webgpu\",kernelFunc:yoe};var boe=xe({opType:X.COSH}),iL={kernelName:Ko,backendName:\"webgpu\",kernelFunc:boe};var tx=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=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) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${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    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${o});\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 = ${n};\n        let width_scale = ${i};\n        let in_y = ${s};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${p};\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    `}};var Coe=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new tx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},uL={kernelName:Xo,backendName:\"webgpu\",kernelFunc:Coe};var Zu;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Zu||(Zu={}));var Kl=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Zu.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${pL(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n      ${Q(\"index\")} {\n       if (index < uniforms.size) {\n         var coords = getCoordsFromIndex(index);\n\n         let end = ${cL(e,\"coords\",this.op)};\n         var val = ${o};\n         let pow2 = i32(pow(2.0, uniforms.index));\n         if (${s}) {\n           let idx = ${a};\n           ${cL(e,\"coords\",this.op)} = idx;\n           val ${this.op}= getX(${pL(e,\"coords\",this.op)});\n         }\n         setOutputAtIndex(index, val);\n       }\n      }\n    `}};function pL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function cL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function rx(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=Nr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Kl(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Kl(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=Nr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function Soe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return rx(Zu.Prod,n,t,s,a,i)}var lL={kernelName:qo,backendName:\"webgpu\",kernelFunc:Soe};function woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return rx(Zu.Sum,n,t,s,a,i)}var mL={kernelName:jo,backendName:\"webgpu\",kernelFunc:woe};function Ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=fr({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Tc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var dL={kernelName:ni,backendName:\"webgpu\",kernelFunc:Ioe};var ox=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Q(\"index\")} {\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 = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new ox(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var fL={kernelName:Yo,backendName:\"webgpu\",kernelFunc:voe};var nx=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, inDims : vec2<i32>,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n      ${pr(this.activation,this.hasPreluActivation,!1,4)}\n\n      var<workgroup> mm_Asub : array<array<f32, ${n}>, ${o}>;\n      var<workgroup> mm_Bsub : array<array<f32, ${this.filterWidth}>, ${this.filterHeight}>;\n      fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n        var value = 0.0;\n        if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n        {\n          value = getX(batch, channel, row, col);\n        }\n        return value;\n      }\n\n      ${Q()} {\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.zw) - uniforms.pad;\n        let channelMul = uniforms.wShape[3];\n        let d1 = coords[1] / channelMul;\n        let q = coords[1] % channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n\n        let localRow = i32(localId.y);\n        let localCol = i32(localId.x);\n\n        // Load one tile of X into local memory.\n        for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n          for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n            let rowOffset = inputRow - localRow;\n            let colOffset = inputCol - localCol;\n            mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n          }\n        }\n\n        // Load one tile of W into local memory.\n        var wIndex = i32(localIndex);\n        ${e<t?`if (wIndex < ${e})`:`for(; wIndex < ${e}; wIndex = wIndex + ${t})`}\n\n        {\n          let wRow = wIndex / ${this.filterWidth};\n          let wCol = wIndex % ${this.filterWidth};\n          mm_Bsub[wRow][wCol] = getW(wRow, wCol, d1, q);\n        }\n\n        workgroupBarrier();\n\n        var value = 0.0;\n        for (var wR = 0; wR < ${this.filterHeight}; wR = wR + 1) {\n          for (var wC = 0; wC < ${this.filterWidth}; wC = wC + 1) {\n            let xVal = mm_Asub[localRow + wR][localCol + wC];\n            let wVal = mm_Bsub[wR][wC];\n            value = fma(xVal, wVal, value);\n          }\n        }\n        ${Gr(this.addBias,this.activation)}\n        if (coordsInBounds4D(coords, uniforms.outShape)) {\n          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }\n    `}};var $c=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, inDims : vec2<i32>,\",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n      ${pr(this.activation,this.hasPreluActivation,!0,4)}\n      fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {\n        var value = vec4<f32>(0.0);\n        if (col >=0 && col < uniforms.inDims[1]) {\n          value = getX(batch, row, col, channel);\n        }\n        return value;\n      }\n\n      ${Q()} {\n        let batch = i32(globalId.z) / uniforms.outShape[1];\n        let r = i32(globalId.z) % uniforms.outShape[1];\n        let c = i32(globalId.y) * ${this.workPerThread};\n        let d1 = i32(globalId.x) * 4;\n        let xRCCorner = vec2<i32>(r, c) * vec2<i32>(${t}, ${o}) - uniforms.pad;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n        var xVals : array<vec4<f32>, ${e}>;\n        var dotProd : array<vec4<f32>, ${this.workPerThread}>;\n        for (var i = 0; i < ${this.workPerThread}; i++) {\n          dotProd[i] = vec4<f32>(0.0);\n        }\n\n        // Use constant instead of uniform can give better performance.\n        for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n          let xR = xRCorner + wR;\n          if (xR >=0 && xR < uniforms.inDims[0]) {\n            for (var i = 0; i < ${e}; i++) {\n              xVals[i] = readX(batch, xR, xCCorner + i, d1);\n            }\n            for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n              let wValue = getW(wR, wC, d1, 0);\n              for (var i = 0; i < ${this.workPerThread}; i++) {\n                dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n              }\n            }\n          }\n        }\n\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d1);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            var value = dotProd[i];\n            ${Gr(this.addBias,this.activation)}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n          }\n        }\n      }\n    `}};var Ec=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,\n      filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n      ${pr(this.activation,this.hasPreluActivation,!1,4)}\n\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getOutputCoords();\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.stride - uniforms.pad;\n          let d2 = coords[${this.isChannelsLast?3:1}];\n          let channelMul = uniforms.wShape[3];\n          let d1 = d2 / channelMul;\n          let q = d2 % 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)|x(d1, ?, ?) with w(:, :, d1, q) to get\n          // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n          // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n          // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n          var value = 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              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 = ${e};\n                  let wVal = getW(wR, wC, d1, q);\n                  value = value + 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 = ${e};\n                  let wVal = getW(wR, wC, d1, q);\n                  value = value + xVal * wVal;\n                }\n              }\n            }\n            ${Gr(this.addBias,this.activation)}\n          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }\n    `}};function koe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new nx(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new $c(d):(g=new Ec(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var hL={kernelName:Qo,backendName:\"webgpu\",kernelFunc:koe};var sx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getOutputCoords();\n          let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function Noe(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=me({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new sx(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=me({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var gL={kernelName:si,backendName:\"webgpu\",kernelFunc:Noe};var ax=class{constructor(e){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n       ${Q(\"index\")} {\n         if (index < uniforms.size) {\n           let neg_infinity = -3.4e38;\n           let coords = getOutputCoords();\n           let batch = coords.x;\n           let d1 = coords.w;\n           let outTopLeftCorner = coords.yz * uniforms.stride - uniforms.pad;\n           let hBeg = outTopLeftCorner.x;\n           let wBeg = outTopLeftCorner.y;\n\n           var curVal = neg_infinity;\n           for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n             let hIn = hBeg + h * uniforms.dilation[0];\n\n             if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n               for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n                 let wIn = wBeg + w * uniforms.dilation[1];\n\n                 if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n                   let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n                   if (val > curVal) {\n                     curVal = val;\n                   }\n                 }\n               }\n             }\n           }\n\n           setOutputAtIndex(index, curVal);\n         }\n       }\n     `}};function Toe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new ax(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var xL={kernelName:ai,backendName:\"webgpu\",kernelFunc:Toe};var II=Ze({opType:ge.MUL,cpuKernelImpl:lM,supportsComplex:!0}),yL={kernelName:kn,backendName:\"webgpu\",kernelFunc:II};function ql(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,\"sum\",t)}var bL={kernelName:Kn,backendName:\"webgpu\",kernelFunc:ql};function _oe(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h<l;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:b}=S.getEinsumPermutation(d,p[g]),C;S.isIdentityPermutation(x)?C=s[g]:(C=Nr({inputs:{x:s[g]},backend:t,attrs:{perm:x}}),f.push(C));let w=C.shape.slice();for(let k=0;k<b.length;++k)w.splice(b[k],0,1);y.arraysEqual(C.shape,w)||(C=me({inputs:{x:C},backend:t,attrs:{shape:w}}),f.push(C)),m===null?m=C:(m=II({inputs:{a:C,b:m},backend:t}),f.push(m))}h<l-1&&(u[h]>=0&&(m=ql({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var CL={kernelName:ii,backendName:\"webgpu\",kernelFunc:_oe};var $oe=xe({opType:X.ELU}),SL={kernelName:Jo,backendName:\"webgpu\",kernelFunc:$oe};var Eoe=Ze({opType:ge.EQUAL,dtype:\"bool\",cpuKernelImpl:Q3}),wL={kernelName:en,backendName:\"webgpu\",kernelFunc:Eoe};var Aoe=xe({opType:X.ERF}),IL={kernelName:da,backendName:\"webgpu\",kernelFunc:Aoe};var vI=xe({opType:X.EXP,cpuKernelImpl:Z3,dtype:\"float32\"}),vL={kernelName:tn,backendName:\"webgpu\",kernelFunc:vI};function ix(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),me({inputs:{x:s},backend:o,attrs:{shape:i}})}var kL={kernelName:Ss,backendName:\"webgpu\",kernelFunc:ix};var Roe=xe({opType:X.EXPM1,cpuKernelImpl:J3}),NL={kernelName:fa,backendName:\"webgpu\",kernelFunc:Roe};var jl=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n    fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n      ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n    }\n\n    fn mulMatDFT(batch: i32, index: i32) -> f32 {\n      let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n      let exponentMultiplierTimesIndexRatio =\n          uniforms.exponentMultiplier * indexRatio;\n\n      var result = 0.0;\n\n      for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n        // x = (-2|2 * PI / N) * index * i;\n        let x = exponentMultiplierTimesIndexRatio * f32(i);\n        let expR = cos(x);\n        let expI = sin(x);\n        let real = getReal(batch, i);\n        let imag = getImag(batch, i);\n\n        result = result +\n            unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n      }\n\n      return result;\n    }\n\n    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getOutputCoords();\n        setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n      }\n    }\n  `}};function ux(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=me({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new jl(\"real\",u),l=new jl(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=po({inputs:{real:g,imag:x},backend:t});i.push(b);let C=me({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(w=>t.disposeData(w.dataId)),C}function Doe(r){let{inputs:e,backend:t}=r,{input:o}=e;return ux(o,!1,t)}var TL={kernelName:ui,backendName:\"webgpu\",kernelFunc:Doe};var px=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}};var _L={kernelName:rn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new px(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var Foe=xe({opType:X.FLOOR,cpuKernelImpl:eM}),$L={kernelName:on,backendName:\"webgpu\",kernelFunc:Foe};var Ooe=Ze({opType:ge.INT_DIV,dtype:\"int32\"}),EL={kernelName:nn,backendName:\"webgpu\",kernelFunc:Ooe};var cx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d<f32>\"};\n      ${Q(\"index\")} {\n        let flatIndex = index * uniforms.numChannels;\n        if (flatIndex < uniforms.size) {\n          let coords = getCoordsFromIndex(flatIndex);\n          let values = ${e};\n          for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n            result[flatIndex + i] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}};var AL={kernelName:Zi,backendName:\"webgpu\",kernelFunc:Poe},Ac,kI=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\"),lx=new Map;function Poe(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d){let D=n;if(!lx.has(D)||lx.get(D).expired){let P={source:D};lx.set(D,t.device.importExternalTexture(P))}b={width:c,height:l,format:null,usage:null,texture:lx.get(D)}}else{if(f){let L=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Ac==null||L!==kI)&&(kI=L,Ac=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:kI})),Ac.canvas.width=c,Ac.canvas.height=l,Ac.drawImage(n,0,0,c,l),n=Ac.canvas}let D=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,P=\"rgba8unorm\",M=t.textureManager.acquireTexture(m[1],m[0],P,D);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b={width:c,height:l,format:P,usage:D,texture:M}}let C=y.sizeFromShape(m),w=y.computeStrides(m),k=new cx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...w]}],E=t.makeTensorInfo([l,c],\"int32\"),A=t.tensorMap.get(E.dataId);A.resourceInfo=b;let R=t.runWebGPUProgram(k,[E],\"int32\",_);return t.disposeData(E.dataId),R}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let w=0;w<b;w++)w%4<s&&(g[C++]=h[w])}let x=t.makeTensorInfo(m,\"int32\",new Int32Array(g));return t.uploadToGPU(x.dataId),x}var mx=class{constructor(e,t,o,n,s){this.uniforms=\"varianceEpsilon : f32,\",this.workgroupSize=[128,1,1],this.size=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o),this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\")),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\")),this.offsetShape=n,this.scaleShape=s,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      ${Q(\"index\")} {\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  `}};var RL={kernelName:sn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new mx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function Moe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return Qg({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var DL={kernelName:go,backendName:\"webgpu\",kernelFunc:Moe};function Loe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=S.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],w;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?w=new $c(h,x,m,b):(w=new Ec(h,x,m,b),C.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(C.push({type:\"float32\",data:[d]}),w.uniforms+=\" alpha : f32,\"),t.runWebGPUProgram(w,g,\"float32\",C)}var FL={kernelName:xo,backendName:\"webgpu\",kernelFunc:Loe};var dx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Rt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n      ${Q(\"index\")} {\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 Boe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=S.prepareAndValidate(o,n),m=me({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=me({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=t.readSync(n.dataId),C=t.bufferSync(o),w=tM(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,w.values)}let f=new dx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=me({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var OL={kernelName:an,backendName:\"webgpu\",kernelFunc:Boe};var fx=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=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Voe(this.aShape);return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${e}));\n        }\n      }\n    `}};function Voe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;o<r.length;o++)o===2?t.push(\"indexZ\"):t.push(`${e[o]}`);return t.join()}function NI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=me({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=me({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])){let C=t.tensorMap.get(d.dataId).values,w=le(d.shape,d.dtype,C),_=t.tensorMap.get(m.dataId).values,E=le(m.shape,m.dtype,_),A=rM(E,w,f);return l.forEach(R=>t.disposeData(R.dataId)),t.makeTensorInfo(u.outputShape,A.dtype,A.values)}let h=new fx(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=me({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var PL={kernelName:Is,backendName:\"webgpu\",kernelFunc:NI};var zoe=Ze({opType:ge.GREATER,cpuKernelImpl:nM,dtype:\"bool\"}),ML={kernelName:un,backendName:\"webgpu\",kernelFunc:zoe};var Woe=Ze({opType:ge.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:oM}),LL={kernelName:pn,backendName:\"webgpu\",kernelFunc:Woe};function Uoe(r){let{inputs:e,backend:t}=r,{input:o}=e;return ux(o,!0,t)}var BL={kernelName:pi,backendName:\"webgpu\",kernelFunc:Uoe};var Goe=xe({opType:X.IS_FINITE,dtype:\"bool\"}),VL={kernelName:ha,backendName:\"webgpu\",kernelFunc:Goe};var Hoe=xe({opType:X.IS_INF,dtype:\"bool\"}),zL={kernelName:ga,backendName:\"webgpu\",kernelFunc:Hoe};var Koe=xe({opType:X.IS_NAN,dtype:\"bool\"}),WL={kernelName:cn,backendName:\"webgpu\",kernelFunc:Koe};function qoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Hr(n.shape,X.LEAKYRELU,\"alpha : f32,\");return t.runWebGPUProgram(i,[n],\"float32\",a)}var UL={kernelName:ln,backendName:\"webgpu\",kernelFunc:qoe};var joe=Ze({opType:ge.LESS,dtype:\"bool\",cpuKernelImpl:aM}),GL={kernelName:mn,backendName:\"webgpu\",kernelFunc:joe};var Xoe=Ze({opType:ge.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:sM}),HL={kernelName:dn,backendName:\"webgpu\",kernelFunc:Xoe};var hx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n        }\n      }\n    `}};function Yoe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new hx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return e.runWebGPUProgram(i,[],\"float32\",p)}var KL={kernelName:li,backendName:\"webgpu\",kernelFunc:Yoe};var Qoe=xe({opType:X.LOG,cpuKernelImpl:iM}),qL={kernelName:fn,backendName:\"webgpu\",kernelFunc:Qoe};var Zoe=xe({opType:X.LOG1P}),jL={kernelName:xa,backendName:\"webgpu\",kernelFunc:Zoe};var Joe=Ze({opType:ge.LOGICAL_AND,dtype:\"bool\"}),XL={kernelName:hn,backendName:\"webgpu\",kernelFunc:Joe};var ene=xe({opType:X.LOGICAL_NOT}),YL={kernelName:gn,backendName:\"webgpu\",kernelFunc:ene};var tne=Ze({opType:ge.LOGICAL_OR}),QL={kernelName:xn,backendName:\"webgpu\",kernelFunc:tne};var ZL=`\n  var powValue = 0.0;\n  let basis = uniforms.bias + uniforms.alpha * sum;\n  if (uniforms.beta == 0.5) {\n    powValue = inverseSqrt(basis);\n  } else if (uniforms.beta == 1.0) {\n    powValue = 1.0 / basis;\n  } else {\n    powValue = exp(log(basis) * (-uniforms.beta));\n  }\n`,gx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n    ${Q(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getOutputCoords();\n        let b = coords[0];\n        let r = coords[1];\n        let c = coords[2];\n        let d = coords[3];\n\n        let x = getX(b, r, c, d);\n        var sum = 0.0;\n        for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n          let idx = d + i;\n          if (idx >= 0 && idx < uniforms.xShape[3]) {\n            let z = getX(b, r, c, idx);\n            sum = sum + z * z;\n          }\n        }\n        ${ZL}\n\n        setOutputAtIndex(index, x * powValue);\n      }\n    }\n  `}},xx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Y(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n    var <workgroup>lrnSub: array<f32, ${this.workgroupSize[0]}>;\n    const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n    const maxAllowRadius = ${this.maxAllowRadius};\n\n    ${Q()} {\n      let localDepth = i32(localId.x);\n      let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n      let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n      let b = i32(globalId.z) / uniforms.xShape[1];\n      let r = i32(globalId.z) - b * uniforms.xShape[1];\n      let c = i32(globalId.y);\n      let d = workgroupDepth + localDepth;\n\n      var x = 0.0;\n      if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n        x = getX(b, r, c, xDepth);\n      }\n      lrnSub[localDepth] = x;\n      workgroupBarrier();\n\n      if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n        var sum = 0.0;\n        let index = localDepth + maxAllowRadius;\n        for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n          let z = lrnSub[index + i];\n          sum = sum + z * z;\n        }\n        ${ZL}\n\n        setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n      }\n    } `}};function rne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new gx(n.shape):u=new xx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var JL={kernelName:mi,backendName:\"webgpu\",kernelFunc:rne};var one=Ze({opType:ge.MAX,cpuKernelImpl:pM}),eB={kernelName:bn,backendName:\"webgpu\",kernelFunc:one};function nne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return zg(n,c,\"max\",t)}var tB={kernelName:Cn,backendName:\"webgpu\",kernelFunc:nne};function sne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,\"min\",t)}var rB={kernelName:wn,backendName:\"webgpu\",kernelFunc:sne};var ane=Ze({opType:ge.MIN,cpuKernelImpl:cM}),oB={kernelName:In,backendName:\"webgpu\",kernelFunc:ane};var yx=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:\"\"}`).join(\",\"),n=e===1?\"start\":\"start[i]\",s=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=Rt(e),p=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let start = ${i}(${t});\n          let end = ${i}(${o});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${e}; i = i + 1) {\n            if (${a} < ${n}) {\n              ${a} = ${n} * 2 - ${a} - ${this.offset};\n            } else if(${a} >= ${s}) {\n              ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${p}));\n        }\n      }\n    `}};var nB={kernelName:vn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var ine=Ze({opType:ge.MOD}),sB={kernelName:ya,backendName:\"webgpu\",kernelFunc:ine};function une(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=mM(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Hr(o.shape,X.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var aB={kernelName:vs,backendName:\"webgpu\",kernelFunc:une};function pne(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var iB={kernelName:Tn,backendName:\"webgpu\",kernelFunc:pne};function cne(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var uB={kernelName:_n,backendName:\"webgpu\",kernelFunc:cne};var bx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n      ${Q(\"index\")} {\n        if(index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n                                      f32(i32(round(getX(coords.x))) == coords.y)));\n        }\n      }\n    `}};function lne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new bx(u,a),l=me({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=me({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var pB={kernelName:$n,backendName:\"webgpu\",kernelFunc:lne};function Xl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=Xa({inputs:{input:o},backend:t}),s=Xl({inputs:{x:n},backend:t}),a=Qu({inputs:{input:o},backend:t}),i=Xl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return fr({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var cB={kernelName:Os,backendName:\"webgpu\",kernelFunc:Xl};function lB(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=Xa({inputs:{input:o},backend:t}),s=lB({inputs:{x:n},backend:t}),a=Qu({inputs:{input:o},backend:t}),i=Xl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return fr({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var mB={kernelName:ks,backendName:\"webgpu\",kernelFunc:lB};function mne(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return ix({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=ix({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=wI({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var dB={kernelName:Ns,backendName:\"webgpu\",kernelFunc:mne};var Cx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Rt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:\"\"}`).join(\",\"),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?\"any(outC < start)\":\"outC < start\",p=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let start = ${s};\n          let end = ${a};\n          let outC = getCoordsFromIndex(index);\n\n          if (${i} || ${p}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${u}));\n          }\n        }\n      }\n    `}};var TI=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Dt({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return fr({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new Cx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},fB={kernelName:En,backendName:\"webgpu\",kernelFunc:TI};var dne=Ze({opType:ge.POW}),hB={kernelName:An,backendName:\"webgpu\",kernelFunc:dne};function fne(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Yu(ge.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var gB={kernelName:Rn,backendName:\"webgpu\",kernelFunc:fne};function hne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return Kr(n,s,a,\"prod\",t)}var xB={kernelName:Dn,backendName:\"webgpu\",kernelFunc:hne};var gne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=hM(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},yB={kernelName:Ts,backendName:\"webgpu\",kernelFunc:gne};var _I=Ze({opType:ge.DIV}),bB={kernelName:Zo,backendName:\"webgpu\",kernelFunc:_I};var xne=xe({opType:X.RECIPROCAL}),CB={kernelName:Fn,backendName:\"webgpu\",kernelFunc:xne};var yne=xe({opType:X.RELU}),SB={kernelName:On,backendName:\"webgpu\",kernelFunc:yne};var bne=xe({opType:X.RELU6}),wB={kernelName:Ln,backendName:\"webgpu\",kernelFunc:bne};var Sx=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Q(\"index\")} {\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 Cne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new Sx(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var IB={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Cne};var wx=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}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      ${Q(\"index\")} {\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 Sne(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new wx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var vB={kernelName:Pn,backendName:\"webgpu\",kernelFunc:Sne};var Ix=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4<i32>,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n      \n      // Using uniform variables as judging conditions, so the function has\n      // coherent execution within all threads.\n      fn getReverseCoords(coords : vec4<i32>) -> vec4<i32> {\n        var reverseCoords = coords;\n        if (uniforms.axis[0] == 1) {\n          reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n        }\n        if (uniforms.axis[1] == 1) {\n          reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n        }\n        if (uniforms.axis[2] == 1) {\n          reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n        }\n        if (uniforms.axis[3] == 1) {\n          reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n        }\n\n        return reverseCoords;\n      }\n    \n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let reverseCoords = getReverseCoords(coords);\n          setOutputAtIndex(index, getX(reverseCoords[0],\n              reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n        }\n      }\n    `}};function wne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Dt({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=me({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new Ix(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=me({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var kB={kernelName:Bn,backendName:\"webgpu\",kernelFunc:wne};var vx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(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        ${Q(\"index\")} {\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            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}};var NB={kernelName:ts,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new vx(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Ine=xe({opType:X.ROUND}),TB={kernelName:Vn,backendName:\"webgpu\",kernelFunc:Ine};var vne=xe({opType:X.RSQRT,cpuKernelImpl:gM}),_B={kernelName:zn,backendName:\"webgpu\",kernelFunc:vne};var Hi=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=ae(e),this.dispatch=Y(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Rt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.dispatchLayout.x.length===2&&(n=\"vec2<i32>(flattenedIndex, coords[1])\",s=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        // N.B. |updates| could be a scalar tensor, conceptually representing a\n        // 2D tensor with all values equal to that. By design, its size must be\n        // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n        // gives the other.\n        let sliceSize = uniforms.outShape[1];\n        let d0 = index / sliceSize;\n        let d1 = index - d0 * sliceSize;\n        return vec2<i32>(d0, d1);\n      }\n      `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n    ${s}\n      ${Q(\"index\")} {\n        if (index < uniforms.updatesSize) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${o};\n          }\n          let updateValue =\n              ${vc(this.type,!1)}(${i});\n          let flatIndex = getOutputIndexFromCoords(${n});\n\n          ${this.sumDupeIndices?Ic(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast<i32>(updateValue));\"}\n        }\n      }`}};function kne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=me({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=me({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=fr({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new Hi(f.shape,i,d.shape.length,f.shape.length,c,m,h),w=t.runWebGPUProgram(C,[f,d],h,b,g),k=me({inputs:{x:w},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(w.dataId),k}var $B={kernelName:Wn,backendName:\"webgpu\",kernelFunc:kne};var kx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n      fn findBound(batch: i32, value: f32) -> i32 {\n        var left = i32(0);\n        var right = uniforms.numInputs;\n        while (left < right) {\n          var mid = (left + right) / 2;\n          if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n            left = mid + 1;\n          } else {\n            right = mid;\n          }\n        }\n        return right;\n      }\n\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let value = getValuesByOutputIndex(index);\n          setOutputAtIndexI32(index, findBound(coords[0], value));\n        }\n      }\n    `}};function Nne(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new kx([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var EB={kernelName:fi,backendName:\"webgpu\",kernelFunc:Nne};var Nx=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${n[i]}`),i<this.cRank&&s.push(`${n[i]}`);e=s.join(),t=a.join()}return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function Tne(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new Nx(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var AB={kernelName:$s,backendName:\"webgpu\",kernelFunc:Tne};var _ne=xe({opType:X.SELU}),RB={kernelName:Ca,backendName:\"webgpu\",kernelFunc:_ne};var $ne=xe({opType:X.SIGMOID}),DB={kernelName:Gn,backendName:\"webgpu\",kernelFunc:$ne};var Ene=xe({opType:X.SIGN}),FB={kernelName:wa,backendName:\"webgpu\",kernelFunc:Ene};var Ane=xe({opType:X.SIN}),OB={kernelName:Un,backendName:\"webgpu\",kernelFunc:Ane};var Rne=xe({opType:X.SINH}),PB={kernelName:Sa,backendName:\"webgpu\",kernelFunc:Rne};var $I=Ze({opType:ge.SUB,cpuKernelImpl:wM,supportsComplex:!0}),MB={kernelName:Yn,backendName:\"webgpu\",kernelFunc:$I};function Dne(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Hl({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=me({inputs:{x:i},backend:t,attrs:{shape:p}}),c=$I({inputs:{a:n,b:u},backend:t}),l=vI({inputs:{x:c},backend:t}),m=ql({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=me({inputs:{x:m},backend:t,attrs:{shape:p}}),f=_I({inputs:{a:l,b:d},backend:t});return t.disposeData(i.dataId),t.disposeData(u.dataId),t.disposeData(c.dataId),t.disposeData(l.dataId),t.disposeData(m.dataId),t.disposeData(d.dataId),f}var LB={kernelName:qn,backendName:\"webgpu\",kernelFunc:Dne};var Fne=xe({opType:X.SOFTPLUS}),BB={kernelName:Ia,backendName:\"webgpu\",kernelFunc:Fne};var One=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;x<n.shape.length;++x)p.push([0,0]);let u=[],c=TI({inputs:{x:n},backend:t,attrs:{paddings:p,constantValue:0}}),l=S.getReshaped(c.shape,s,i,!1),m=S.getPermuted(l.length,s.length,!1),d=S.getReshapedPermuted(c.shape,s,i,!1),f=me({inputs:{x:c},backend:t,attrs:{shape:l}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:m}}),g=me({inputs:{x:h},backend:t,attrs:{shape:d}});return u.push(c),u.push(f),u.push(h),u.forEach(x=>t.disposeData(x.dataId)),g},VB={kernelName:As,backendName:\"webgpu\",kernelFunc:One};var Tx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n<o.length;n++)o[n]=e[n]*t[n];this.outputShape=o,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=Pne(this.rank,\"uniforms.\");return`\n      ${Q(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function Pne(r,e=\"\"){if(r>=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n<r;n++)o.push(`(${t[n]} % ${e}aShape[${n}])`);return o.join()}function EI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"||n.shape.length>=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=IM(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Tx(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var zB={kernelName:to,backendName:\"webgpu\",kernelFunc:EI};function Mne(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let A=t.bufferSync(n),R=t.bufferSync(s),D=y.decodeString(t.readSync(a.dataId)[0]),P=xM(A,R,i,m,c,u,p,l,D,d);return t.makeTensorInfo(i,P.dtype,P.values)}let f=[m/c,c],h=me({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?me({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):Dt({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=me({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),w=EI({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let A=new Hi([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(A,[g,h],x,_,w)}break;default:{let A=new Hi([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(A,[b,h],x,_,w)}{let A=new Hi([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(A,[g,h],x,_,w)}}let E=me({inputs:{x:w},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(w.dataId),E}var WB={kernelName:yi,backendName:\"webgpu\",kernelFunc:Mne};function Lne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=fs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var UB={kernelName:Rs,backendName:\"webgpu\",kernelFunc:Lne};var Bne=xe({opType:X.SQRT}),GB={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Bne};var HB={kernelName:bi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Hr(t.shape,X.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Vne=Ze({opType:ge.SQUARED_DIFFERENCE}),KB={kernelName:jn,backendName:\"webgpu\",kernelFunc:Vne};function zne({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Hr(o.shape,X.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var qB={kernelName:fo,backendName:\"webgpu\",kernelFunc:zne};var _x=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Rt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Q(\"index\")} {\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function Wne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=me({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),E=fs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=me({inputs:{x:E},backend:t,attrs:{shape:f}}),t.disposeData(E.dataId)}else if(t.shouldExecuteOnCPU([n])){let E=t.readSync(n.dataId),A=le(n.shape,n.dtype,E),R=CM(d,A,w,b);k=t.makeTensorInfo(f,n.dtype,R.values)}else{let E=new _x(d),A=[{type:\"int32\",data:b},{type:\"int32\",data:w}],R=t.runWebGPUProgram(E,[n],n.dtype,A);k=me({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeData(R.dataId)}return k}var jB={kernelName:Xn,backendName:\"webgpu\",kernelFunc:Wne};function Une(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=SM(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var XB={kernelName:Ds,backendName:\"webgpu\",kernelFunc:Une};var Gne=xe({opType:X.TAN}),YB={kernelName:Qn,backendName:\"webgpu\",kernelFunc:Gne};var Hne=xe({opType:X.TANH}),QB={kernelName:Zn,backendName:\"webgpu\",kernelFunc:Hne};var $x=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(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        ${Q(\"index\")} {\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      `}},Ex=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ae(this.outputShape),this.dispatch=Y(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Q(\"index\")} {\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 Rc(r,e){e!==null&&r.disposeData(e.dataId)}function ZB(r){let e=1;for(;e<r;)e*=2;return e}function Kne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=n.shape,p=i[i.length-1];if(t.shouldExecuteOnCPU([n])){let k=t.readSync(n.dataId),[_,E]=vM(k,i,n.dtype,s,a);return[t.makeTensorInfo(_.shape,_.dtype,_.values),t.makeTensorInfo(E.shape,E.dtype,E.values)]}if(s===0)return i[i.length-1]=0,[t.makeTensorInfo(i,n.dtype,[]),t.makeTensorInfo(i,\"int32\",[])];if(p===1)return[n,fr({attrs:{shape:i,dtype:\"int32\",value:0},backend:t})];let c=y.sizeFromShape(i)/p,l=me({inputs:{x:n},attrs:{shape:[c,p]},backend:t}),m=ZB(s),d=ZB(p),f=null,h=()=>f===null?[l,l]:[l,f],g=(k,_,E)=>{let A=h(),R=new $x(E),P=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(R,A,\"int32\",P),Rc(t,M)};for(let k=1;k<m;k*=2){let _=k*2;for(let E=k;E>=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new Ex([c,k/2]),R=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],D=f;f=t.runWebGPUProgram(E,_,\"int32\",R),Rc(t,D);let P=m/2,M=P*2;for(let L=P;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=fs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),Rc(t,x);let b=NI({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});Rc(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=me({inputs:{x:f},attrs:{shape:C},backend:t}),Rc(t,x);let w=b;return b=me({inputs:{x:b},attrs:{shape:C},backend:t}),Rc(t,w),[b,f]}var JB={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Kne};var Ax=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=ae(this.outputShape),this.dispatch=Y(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          ${Q(\"index\")} {\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 qne(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Ax(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let w=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",w)}var eV={kernelName:es,backendName:\"webgpu\",kernelFunc:qne};function jne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let l=[],m=new Array(i).fill(0),d=a.shape.slice();d[s]=1;let f=new Array(p);for(let h=0;h<f.length;h++){m[s]=h;let g=fs({inputs:{x:a},backend:t,attrs:{begin:m,size:d}}),x=me({inputs:{x:g},backend:t,attrs:{shape:u}});f[h]=x,l.push(g)}return l.forEach(h=>t.disposeData(h.dataId)),f}var tV={kernelName:Fs,backendName:\"webgpu\",kernelFunc:jne};var Xne=[G3,NM,TM,_M,$M,EM,RM,DM,FM,OM,PM,MM,LM,BM,VM,UM,GM,HM,qM,jM,ZM,JM,eL,K3,rL,oL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,CL,SL,wL,IL,vL,kL,NL,TL,W3,_L,AL,$L,EL,RL,DL,FL,OL,PL,ML,LL,H3,BL,tL,VL,zL,WL,UL,GL,HL,KL,jL,qL,XL,YL,QL,JL,zM,eB,tB,WM,rB,oB,nB,sB,yL,aB,iB,uB,XM,pB,mB,dB,fB,hB,gB,xB,yB,YM,bB,CB,SB,wB,U3,IB,vB,kB,NB,TB,_B,$B,EB,AB,RB,DB,FB,OB,PB,KM,qB,jB,XB,LB,BB,VB,WB,UB,GB,HB,KB,MB,bL,YB,QB,zB,JB,eV,AM,tV,cB];for(let r of Xne)ka(r);var rV=\"4.2.0\",Yne=\"4.2.0\",Qne=\"4.2.0\",Zne=\"4.2.0\",Jne=\"4.2.0\",ese=\"0.0.1-alpha.17\",tse={tfjs:rV,\"tfjs-core\":rV,\"tfjs-converter\":Yne,\"tfjs-backend-cpu\":Qne,\"tfjs-backend-webgl\":Zne,\"tfjs-backend-wasm\":Jne,\"tfjs-backend-webgpu\":ese};export{ys as Abs,aa as Acos,ia as Acosh,yu as AdadeltaOptimizer,bu as AdagradOptimizer,Cu as AdamOptimizer,Su as AdamaxOptimizer,eo as Add,Po as AddN,Mo as All,Lo as Any,Bo as ArgMax,ei as ArgMin,ua as Asin,pa as Asinh,ca as Atan,ma as Atan2,la as Atanh,Vo as AvgPool,dp as AvgPool3D,vm as AvgPool3DGrad,mp as AvgPoolGrad,Ll as BackendWasm,zo as BatchMatMul,bs as BatchToSpaceND,ti as Bincount,fp as BroadcastArgs,ise as BroadcastTo,co as Cast,Wo as Ceil,lo as ClipByValue,ri as Complex,hp as ComplexAbs,Cs as Concat,Uo as Conv2D,oi as Conv2DBackpropFilter,Go as Conv2DBackpropInput,gp as Conv3D,km as Conv3DBackpropFilterV2,xp as Conv3DBackpropInputV2,Ho as Cos,Ko as Cosh,Xo as CropAndResize,qo as Cumprod,jo as Cumsum,Fo as DataStorage,ni as DenseBincount,Yo as DepthToSpace,Qo as DepthwiseConv2dNative,yp as DepthwiseConv2dNativeBackpropFilter,bp as DepthwiseConv2dNativeBackpropInput,si as Diag,ai as Dilation2D,$b as Dilation2DBackpropFilter,_b as Dilation2DBackpropInput,kb as ENV,ii as Einsum,Jo as Elu,Nm as EluGrad,Uc as Environment,en as Equal,da as Erf,tn as Exp,Ss as ExpandDims,fa as Expm1,ui as FFT,ws as Fill,rn as FlipLeftRight,on as Floor,nn as FloorDiv,Zi as FromPixels,sn as FusedBatchNorm,go as FusedConv2D,xo as FusedDepthwiseConv2D,Lu as GPGPUContext,an as GatherNd,Is as GatherV2,fl as GraphModel,un as Greater,pn as GreaterEqual,pi as IFFT,mo as Identity,ci as Imag,ha as IsFinite,ga as IsInf,cn as IsNan,Zr as KernelBackend,mi as LRN,Tm as LRNGrad,ln as LeakyRelu,mn as Less,dn as LessEqual,li as LinSpace,fn as Log,xa as Log1p,use as LogSoftmax,hn as LogicalAnd,gn as LogicalNot,xn as LogicalOr,XI as LogicalXor,pse as LowerBound,Pi as MathBackendCPU,Vi as MathBackendWebGL,yn as Max,Cn as MaxPool,Cp as MaxPool3D,$m as MaxPool3DGrad,_m as MaxPoolGrad,Sp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,wu as MomentumOptimizer,wp as Multinomial,kn as Multiply,vs as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,jb as OP_SCOPE_SUFFIX,$n as OneHot,ks as OnesLike,wr as Optimizer,cl as OptimizerConstructors,Ns as Pack,En as PadV2,cse as Pool,An as Pow,Rn as Prelu,Dn as Prod,Iu as RMSPropOptimizer,Ip as RaggedGather,vp as RaggedRange,kp as RaggedTensorToTensor,Ts as Range,Lb as Rank,di as Real,Zo as RealDiv,Fn as Reciprocal,$t as Reduction,On as Relu,Ln as Relu6,_s as Reshape,Mn as ResizeBilinear,Am as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,ts as RotateWithOffset,Vn as Round,zn as Rsqrt,Pa as SGDOptimizer,Wn as ScatterNd,fi as SearchSorted,$s as Select,Ca as Selu,Gn as Sigmoid,wa as Sign,Un as Sin,Sa as Sinh,Es as Slice,qn as Softmax,Ia as Softplus,As as SpaceToBatchND,hi as SparseFillEmptyRows,va as SparseReshape,gi as SparseSegmentMean,xi as SparseSegmentSum,yi as SparseToDense,Rs as SplitV,Hn as Sqrt,bi as Square,jn as SquaredDifference,fo as Step,Xn as StridedSlice,Ds as StringNGrams,Ci as StringSplit,Si as StringToHashBucketFast,Yn as Sub,Kn as Sum,Qn as Tan,Zn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Jn as TopK,es as Transform,ro as Transpose,Np as Unique,Fs as Unpack,Tp as UnsortedSegmentSum,lse as UpperBound,Na as Variable,Gi as WebGPUBackend,Os as ZerosLike,ho as _FusedMatMul,Yt as abs,G0 as acos,H0 as acosh,be as add,K0 as addN,q0 as all,j0 as any,X0 as argMax,Y0 as argMin,Q0 as asin,Z0 as asinh,J0 as atan,ev as atan2,tv as atanh,qm as avgPool,nv as avgPool3d,Pie as backend,S as backend_util,sv as basicLSTMCell,_i as batchNorm,iv as batchNorm2d,uv as batchNorm3d,pv as batchNorm4d,jm as batchToSpaceND,Xm as bincount,VG as booleanMaskAsync,cv as broadcastArgs,$i as broadcastTo,yr as broadcast_util,gN as browser,le as buffer,je as cast,lv as ceil,mv as clipByValue,Lr as clone,Tr as complex,xt as concat,dv as concat1d,fv as concat2d,hv as concat3d,gv as concat4d,xv as conv1d,Ei as conv2d,yv as conv2dTranspose,bv as conv3d,Sv as conv3dTranspose,bse as copyRegisteredKernels,wv as cos,Iv as cosh,al as cosineWindow,vv as cumprod,kv as cumsum,br as customGrad,Nv as denseBincount,nC as deprecationWarn,Tv as depthToSpace,Bp as depthwiseConv2d,Mq as deregisterOp,ki as device_util,_v as diag,$v as dilation2d,kie as disableDeprecationWarnings,Ot as dispose,Nie as disposeVariables,He as div,Av as divNoNan,Rv as dot,ZG as dropout,Dv as einsum,Jm as elu,vie as enableDebugMode,Iie as enableProdMode,CC as enclosingPowerOfTwo,sr as engine,O as env,Zm as equal,Fv as erf,Mv as euclideanNorm,So as exp,Ra as expandDims,Lv as expm1,ed as eye,zp as fft,Us as fill,Fie as findBackend,Oie as findBackendFactory,td as floor,Km as floorDiv,GE as forceHalfFloat,SC as fused,rd as gather,YG as gatherND,Wd as gather_util,Rie as getBackend,Eb as getGradient,Kc as getKernel,Rm as getKernelsForBackend,Kee as getThreadsCount,$w as gpgpu_util,HU as grad,KU as grads,uu as greater,od as greaterEqual,du as ifft,Ri as imag,eK as image,eH as inTopKAsync,La as io,Td as irfft,Bv as isFinite,Vv as isInf,zv as isNaN,_r as keep,Vt as kernel_impls,nd as leakyRelu,Wv as less,Vp as lessEqual,tK as linalg,Uv as linspace,A6 as loadGraphModel,R6 as loadGraphModelSync,Gv as localResponseNormalization,Da as log,sd as log1p,Hv as logSigmoid,Kv as logSoftmax,ud as logSumExp,pu as logicalAnd,pd as logicalNot,cd as logicalOr,qv as logicalXor,rK as losses,jv as lowerBound,Xe as matMul,fN as math,Gs as max,md as maxPool,Xv as maxPool3d,Yv as maxPoolWithArgmax,dd as maximum,cu as mean,Tie as memory,Qv as meshgrid,rl as min,fd as minimum,Zv as mirrorPad,Jv as mod,ek as moments,UG as movingAverage,ne as mul,tk as multiRNNCell,rk as multinomial,Cr as neg,AC as nextFrame,iu as norm,hd as notEqual,nl as oneHot,Hs as ones,ok as onesLike,N as op,nk as outerProduct,Ks as pad,sk as pad1d,ak as pad2d,ik as pad3d,uk as pad4d,pk as pool,Aa as pow,xd as prelu,Hm as print,ck as prod,_ie as profile,lk as raggedGather,mk as raggedRange,dk as raggedTensorToTensor,fk as rand,Ok as randomGamma,Id as randomNormal,Pk as randomStandardNormal,vd as randomUniform,Di as range,Aie as ready,Fa as real,Mk as reciprocal,Ti as registerBackend,gse as registerGradient,ka as registerKernel,Pq as registerOp,Fi as relu,kd as relu6,Die as removeBackend,W as reshape,no as reverse,Lk as reverse1d,Bk as reverse2d,Vk as reverse3d,zk as reverse4d,Wp as rfft,Nd as round,Wk as rsqrt,Ce as scalar,HG as scatterND,sl as scatter_util,ol as searchSorted,Uk as selu,Gk as separableConv2d,oN as serialization,Eie as setBackend,Mie as setPlatform,Hee as setThreadsCount,Uee as setWasmPath,Gee as setWasmPaths,US as setWebGLContext,Hk as setdiff1dAsync,Zp as shared,Ws as sigmoid,Kk as sign,JH as signal,qk as sin,jk as sinh,Ke as slice,Xk as slice1d,Yk as slice2d,Qk as slice3d,Zk as slice4d,ut as slice_util,Jk as softmax,id as softplus,gd as spaceToBatchND,oK as sparse,jG as sparseToDense,ZH as spectral,Oa as split,$r as sqrt,Qt as square,_d as squaredDifference,Up as squeeze,Sr as stack,$d as step,e1 as stridedSlice,nK as string,ke as sub,et as sum,Ta as sumOutType,t1 as tan,tl as tanh,nr as tensor,dr as tensor1d,Oi as tensor2d,Ed as tensor3d,r1 as tensor4d,o1 as tensor5d,n1 as tensor6d,C0 as tensor_util,Fk as test_util,Ee as tidy,Ai as tile,$ie as time,s1 as topk,YMe as train,Gp as transpose,a1 as truncatedNormal,i1 as unique,yse as unregisterGradient,xse as unregisterKernel,u1 as unsortedSegmentSum,so as unstack,dt as upcastType,p1 as upperBound,y as util,qU as valueAndGrad,jU as valueAndGrads,c1 as variable,pC as variableGrads,tse as version,F6 as version_converter,RK as version_core,pj as version_cpu,qee as version_wasm,nY as version_webgl,CJe as webgl,nc as webgl_util,hI as webgpu_util,ss as where,Rd as whereAsync,Br as zeros,Ut as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${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: number[]) => data.reduce((acc: 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 no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\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 size in pixels of a detected face box before resutls are discared */\n  minSize: 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  /** Keep detected faces that cannot be verified using facemesh */\n  keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig 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/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n  /** minimum confidence for a detected race before results are discarded */\n  minConfidence: number,\n}\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  attention: Partial<FaceAttentionConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n  gear: Partial<FaceGearConfig>,\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  /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n  ratio: number,\n  /** possible rvm segmentation mode */\n  mode: SegmentationEnum,\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  /** apply auto-brighness */\n  autoBrightness: 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/**\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: `webgl` for browser and `tensorflow` for nodejs\n  */\n  backend: BackendEnum,\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: false\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: WarmupEnum,\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  /** Validate kernel ops used in model during model load\n   * default: true\n   * any errors will be printed on console but will be treated as non-fatal\n   */\n  validateModels: 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  /** Explicit flags passed to initialize TFJS */\n  flags: Record<string, unknown>,\n\n  /** Software Kernels\n   * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n  */\n  softwareKernels: boolean,\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  validateModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: false,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  flags: {},\n  softwareKernels: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    autoBrightness: 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: false,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      minSize: 0,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      keepInvalid: false,\n    },\n    attention: {\n      enabled: false,\n      modelPath: 'facemesh-attention.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-lite.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: '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: 'rvm.json',\n    ratio: 0.5,\n    mode: 'default',\n  },\n};\n\nexport { config as defaults };\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\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, 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) || 'unknown'}`);\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);\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) || 'unknown'}`);\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) as HTMLCanvasElement;\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  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\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();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\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;\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;\n  }\n\n  const filter = {\n    colorMatrix: (matrix: number[]) => { // 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: number) => {\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: number) => {\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: number) => {\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: number) => {\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: number[]) => { // 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: number) => {\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: number) => {\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: number) => {\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    const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\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 '../tfjs/types';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const rgb = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n  const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n  // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n  // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n  const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n  const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const maxRange = maxValue > 1 ? 255 : 1;\n  const factor = maxRange / maxValue;\n  let final: Tensor;\n  if (factor > 1) {\n    const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n    const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n    // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n    const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n    const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n    final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n    tf.dispose([...sub, ...range, ...enh]);\n  } else {\n    final = tf.expandDims(squeeze, 0);\n  }\n  tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n  return final;\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, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\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 reset() {\n  last.inputSum = 0;\n  last.cacheDiff = 1;\n  last.sumMethod = 0;\n  last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c: AnyCanvas;\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') {\n        c = document.createElement('canvas');\n        c.width = width;\n        c.height = height;\n      } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\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 react-native without canvas support installed');\n      } else {\n        throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      }\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  // @ts-ignore its either defined or we already threw an error\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: Tensor4D | 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 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 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['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 as Tensor3D, [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 as Tensor4D, [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 as Tensor).shape).toString()}`);\n    if ((tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n  }\n  // check if resizing will be needed\n  if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).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: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n  const originalHeight: number = 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: number = originalWidth;\n  let targetHeight: number = 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 as number;\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 as number;\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?.add) {\n      if (config.debug) log('input process error: cannot initialize filters');\n      env.webgl.supported = false;\n      config.filter.enabled = false;\n      copy(inCanvas, outCanvas); // filter failed to initialize\n      // return { tensor: null, canvas: inCanvas };\n    } else {\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()?.length > 1) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    }\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 as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n    if (env.browser && tf.browser) {\n      pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n    } else {\n      depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n      // const arr = Uint8Array.from(input['data']);\n      const arr = new Uint8Array((input as ImageData).data.buffer);\n      pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).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 as HTMLCanvasElement); // 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 as HTMLCanvasElement);\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: Tensor = tf.cast(pixels, 'float32');\n  const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n  tf.dispose([pixels, casted]);\n\n  if (config.filter.autoBrightness) {\n    const max = tf.max(tensor);\n    const maxVal = await max.data();\n    config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n    tf.dispose(max);\n  }\n\n  return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\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] > 3840 || input.shape[2] > 2160) 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 as Tensor3D, [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  /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n  tensorflow: {\n    version: undefined | string,\n    gpu: undefined | boolean,\n  } = {\n      version: undefined,\n      gpu: undefined,\n    };\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    shader: undefined | string,\n    vendor: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n      shader: undefined,\n      vendor: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | GPUAdapterInfo,\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\n  /** MonkeyPatch for Canvas/Image/ImageData */\n  #canvas: undefined;\n  #image: undefined;\n  #imageData: undefined;\n\n  get Canvas() { return this.#canvas; }\n  set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n  get Image() { return this.#image; }\n  // @ts-ignore monkey-patch;\n  set Image(val) { this.#image = val; globalThis.Image = val; }\n  get ImageData() { return this.#imageData; }\n  // @ts-ignore monkey-patch;\n  set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\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\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n      const agent = navigator.userAgent || '';\n      const raw = agent.match(/\\(([^()]+)\\)/g);\n      if (raw?.[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = agent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\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    try { // backend may not be initialized\n      this.tensorflow = {\n        version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n        gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n      };\n    } catch { /**/ }\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend) {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n    }\n    const c = image.canvas(100, 100);\n    const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n    this.webgl.supported = typeof gl !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && gl) {\n      this.webgl.version = gl.getParameter(gl.VERSION);\n      this.webgl.vendor = gl.getParameter(gl.VENDOR);\n      this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n    }\n    this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      if (this.webgpu.supported) {\n        const adapter = await navigator.gpu.requestAdapter();\n        this.webgpu.adapter = await adapter?.requestAdapterInfo();\n      }\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  updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\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')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\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 } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n  /**\n   * element can be:\n   * - string which indicates dom element id\n   * - actual HTMLVideo dom element\n   * - undefined in which case a new HTMLVideoElement will be created\n   */\n  element: string | HTMLVideoElement | undefined,\n  /** print messages on console */\n  debug: boolean,\n  /** use front or back camera */\n  mode: 'front' | 'back',\n  /** camera crop mode */\n  crop: boolean,\n  /** desired webcam width */\n  width: number,\n  /** desired webcam height */\n  height: number,\n  /** deviceId of the video device to use */\n  id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n  /** current webcam configuration */\n  config: WebCamConfig;\n  /** instance of dom element associated with webcam stream */\n  element: HTMLVideoElement | undefined;\n  /** active webcam stream */\n  stream: MediaStream | undefined;\n  /** enumerated video devices */\n  devices: MediaDeviceInfo[] = [];\n\n  constructor() {\n    this.config = {\n      element: undefined,\n      debug: true,\n      mode: 'front',\n      crop: false,\n      width: 0,\n      height: 0,\n    };\n  }\n\n  /** get active webcam stream track */\n  public get track(): MediaStreamTrack | undefined {\n    if (!this.stream) return undefined;\n    return this.stream.getVideoTracks()[0];\n  }\n\n  /** get webcam capabilities */\n  public get capabilities(): MediaTrackCapabilities | undefined {\n    if (!this.track) return undefined;\n    return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n  }\n\n  /** get webcam constraints */\n  public get constraints(): MediaTrackConstraints | undefined {\n    if (!this.track) return undefined;\n    return this.track.getConstraints ? this.track.getConstraints() : undefined;\n  }\n\n  /** get webcam settings */\n  public get settings(): MediaTrackSettings | undefined {\n    if (!this.stream) return undefined;\n    const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n    return track.getSettings ? track.getSettings() : undefined;\n  }\n\n  /** get webcam label */\n  public get label(): string {\n    if (!this.track) return '';\n    return this.track.label;\n  }\n\n  /** is webcam paused */\n  public get paused(): boolean {\n    return this.element?.paused || false;\n  }\n\n  /** webcam current width */\n  public get width(): number {\n    return this.element?.videoWidth || 0;\n  }\n\n  /** webcam current height */\n  public get height(): number {\n    return this.element?.videoHeight || 0;\n  }\n\n  public enumerate = async (): Promise<MediaDeviceInfo[]> => {\n    try {\n      const devices = await navigator.mediaDevices.enumerateDevices();\n      this.devices = devices.filter((device) => device.kind === 'videoinput');\n    } catch {\n      this.devices = [];\n    }\n    return this.devices;\n  };\n\n  /** start method initializizes webcam stream and associates it with a dom video element */\n  public start = async (webcamConfig?: Partial<WebCamConfig>): Promise<void> => {\n    // set config\n    if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n    if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n    if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n    if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n    if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n    if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n    // use or create dom element\n    if (webcamConfig?.element) {\n      if (typeof webcamConfig.element === 'string') {\n        const el = document.getElementById(webcamConfig.element);\n        if (el && el instanceof HTMLVideoElement) {\n          this.element = el;\n        } else {\n          if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n          return;\n        }\n      } else if (webcamConfig.element instanceof HTMLVideoElement) {\n        this.element = webcamConfig.element;\n      } else {\n        if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n        return;\n      }\n    } else {\n      this.element = document.createElement('video');\n    }\n\n    // set constraints to use\n    const requestedConstraints: MediaStreamConstraints = {\n      audio: false,\n      video: {\n        facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n        // @ts-ignore // resizeMode is still not defined in tslib\n        resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n      },\n    };\n    if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n    if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n    if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n    // set default event listeners\n    this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n    this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n    this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n      if (!this.element || !this.stream) return;\n      if (this.element.paused) await this.element.play();\n      else this.element.pause();\n    });\n\n    // get webcam and set it to run in dom element\n    if (!navigator?.mediaDevices) {\n      if (this.config.debug) log('webcam', 'no devices');\n      return;\n    }\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n    } catch (err) {\n      log('webcam', err);\n      return;\n    }\n    if (!this.stream) {\n      if (this.config.debug) log('webcam', 'no stream');\n      return;\n    }\n    this.element.srcObject = this.stream; // assign it to dom element\n    const ready = new Promise((resolve) => { // wait until stream is ready\n      if (!this.element) resolve(false);\n      else this.element.onloadeddata = () => resolve(true);\n    });\n    await ready;\n    await this.element.play(); // start playing\n\n    if (this.config.debug) {\n      log('webcam', {\n        width: this.width,\n        height: this.height,\n        label: this.label,\n        stream: this.stream,\n        track: this.track,\n        settings: this.settings,\n        constraints: this.constraints,\n        capabilities: this.capabilities,\n      });\n    }\n  };\n\n  /** pause webcam video method */\n  public pause = (): void => {\n    if (this.element) this.element.pause();\n  };\n\n  /** play webcam video method */\n  public play = async (): Promise<void> => {\n    if (this.element) await this.element.play();\n  };\n\n  /** stop method stops active webcam stream track and disconnects webcam */\n  public stop = (): void => {\n    if (this.config.debug) log('webcam', 'stop');\n    if (this.track) this.track.stop();\n  };\n}\n", "{\n  \"antispoof\": 853098,\n  \"blazeface\": 538928,\n  \"centernet\": 4030290,\n  \"emotion\": 820516,\n  \"facemesh\": 1477958,\n  \"faceres\": 6978814,\n  \"handlandmark-lite\": 2023432,\n  \"handtrack\": 2964837,\n  \"iris\": 2599092,\n  \"liveness\": 592976,\n  \"models\": 0,\n  \"movenet-lightning\": 4650216,\n  \"affectnet-mobilenet\": 6920630,\n  \"age\": 161240,\n  \"blazeface-back\": 538928,\n  \"blazeface-front\": 402048,\n  \"blazepose-detector\": 5928856,\n  \"blazepose-full\": 6339202,\n  \"blazepose-heavy\": 27502466,\n  \"blazepose-lite\": 2726402,\n  \"efficientpose\": 5651240,\n  \"faceboxes\": 2013002,\n  \"facemesh-attention-pinto\": 2387598,\n  \"facemesh-attention\": 2382414,\n  \"facemesh-detection-full\": 1026192,\n  \"facemesh-detection-short\": 201268,\n  \"faceres-deep\": 13957620,\n  \"gear-e1\": 112438,\n  \"gear-e2\": 112438,\n  \"gear\": 1498916,\n  \"gender-ssrnet-imdb\": 161236,\n  \"gender\": 201808,\n  \"handdetect\": 3515612,\n  \"handlandmark-full\": 5431368,\n  \"handlandmark-sparse\": 5286322,\n  \"handskeleton\": 5502280,\n  \"meet\": 372228,\n  \"mobileface\": 2183192,\n  \"mobilefacenet\": 5171976,\n  \"movenet-multipose\": 9448838,\n  \"movenet-thunder\": 12477112,\n  \"nanodet\": 7574558,\n  \"posenet\": 5032780,\n  \"rvm\": 3739355,\n  \"selfie\": 212886,\n  \"anti-spoofing\": 853098,\n  \"efficientpose-i-lite\": 2269064,\n  \"efficientpose-ii-lite\": 5651240,\n  \"efficientpose-iv\": 25643252,\n  \"insightface-efficientnet-b0\": 13013224,\n  \"insightface-ghostnet-strides1\": 8093408,\n  \"insightface-ghostnet-strides2\": 8049584,\n  \"insightface-mobilenet-emore\": 6938536,\n  \"insightface-mobilenet-swish\": 12168584,\n  \"nanodet-e\": 12319156,\n  \"nanodet-g\": 7574558,\n  \"nanodet-m\": 1887474,\n  \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n  cacheModels: true,\n  cacheSupported: true,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nexport interface ModelInfo {\n  name: string,\n  inCache: boolean,\n  sizeDesired: number,\n  sizeFromManifest: number,\n  sizeLoadedWeights: number,\n  url: string,\n}\n\nexport const modelStats: Record<string, ModelInfo> = {};\n\nasync function httpHandler(url: string, init?: RequestInit): 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  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n  const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n  modelStats[shortModelName] = {\n    name: shortModelName,\n    sizeFromManifest: 0,\n    sizeLoadedWeights: 0,\n    sizeDesired: modelsDefs[shortModelName],\n    inCache: false,\n    url: '',\n  };\n  options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n  let cachedModels = {};\n  try {\n    cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n  } catch {\n    options.cacheSupported = false;\n  }\n  modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n  let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    if (options.debug) log('model load handler:', model['handler']);\n  } catch (err) {\n    log('error finding model i/o handler:', modelUrl, err);\n  }\n  try {\n    // @ts-ignore private property\n    const artifacts = await model.handler?.load() || null; // load manifest\n    modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n    if (artifacts) model.loadSync(artifacts); // load weights\n    else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n    // @ts-ignore private property\n    modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n    if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      if (options.debug) log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "{\n  \"name\": \"@vladmandic/human\",\n  \"version\": \"3.0.5\",\n  \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n  \"sideEffects\": false,\n  \"main\": \"dist/human.node.js\",\n  \"module\": \"dist/human.esm.js\",\n  \"browser\": \"dist/human.esm.js\",\n  \"types\": \"types/human.d.ts\",\n  \"exports\": {\n    \"node\": {\n      \"require\": \"./dist/human.node.js\",\n      \"import\": \"./dist/human.node.js\",\n      \"module\": \"./dist/human.node.js\"\n    },\n    \"require\": \"./dist/human.node.js\",\n    \"import\": \"./dist/human.esm.js\",\n    \"script\": \"./dist/human.js\",\n    \"module\": \"./dist/human.esm.js\",\n    \"types\": \"./types/human.d.ts\",\n    \"dist/human\": \"./dist/human.js\",\n    \"dist/human.js\": \"./dist/human.js\",\n    \"dist/human.esm\": \"./dist/human.esm.js\",\n    \"dist/human.esm.js\": \"./dist/human.esm.js\",\n    \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n    \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n    \"dist/human.node\": \"./dist/human.node.js\",\n    \"dist/human.node.js\": \"./dist/human.node.js\",\n    \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n    \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n    \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n    \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\"\n  },\n  \"author\": \"Vladimir Mandic <mandic00@live.com>\",\n  \"bugs\": {\n    \"url\": \"https://github.com/vladmandic/human/issues\"\n  },\n  \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n  \"license\": \"MIT\",\n  \"engines\": {\n    \"node\": \">=14.0.0\"\n  },\n  \"repository\": {\n    \"type\": \"git\",\n    \"url\": \"git+https://github.com/vladmandic/human.git\"\n  },\n  \"scripts\": {\n    \"start\": \"node --no-warnings demo/nodejs/node.js\",\n    \"dev\": \"build --profile development\",\n    \"clean\": \"build --profile clean\",\n    \"build\": \"rimraf test/build.log && node build.js\",\n    \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n    \"lint\": \"eslint *.json *.js src demo test models wiki\",\n    \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n  },\n  \"keywords\": [\n    \"human\",\n    \"human-library\",\n    \"face-detection\",\n    \"faceid\",\n    \"face-geometry\",\n    \"face-embedding\",\n    \"face-recognition\",\n    \"face-description\",\n    \"face-matching\",\n    \"body-tracking\",\n    \"body-segmentation\",\n    \"hand-tracking\",\n    \"iris-tracking\",\n    \"age-estimation\",\n    \"emotion-detection\",\n    \"gender-prediction\",\n    \"gesture-recognition\",\n    \"gaze-tracking\",\n    \"age-gender\",\n    \"tensorflowjs\",\n    \"tfjs\",\n    \"tensorflow\"\n  ],\n  \"devDependencies\": {\n    \"@html-eslint/eslint-plugin\": \"^0.16.0\",\n    \"@html-eslint/parser\": \"^0.16.0\",\n    \"@microsoft/api-extractor\": \"^7.34.3\",\n    \"@tensorflow/tfjs-backend-cpu\": \"^4.2.0\",\n    \"@tensorflow/tfjs-backend-wasm\": \"^4.2.0\",\n    \"@tensorflow/tfjs-backend-webgl\": \"^4.2.0\",\n    \"@tensorflow/tfjs-backend-webgpu\": \"0.0.1-alpha.17\",\n    \"@tensorflow/tfjs-converter\": \"^4.2.0\",\n    \"@tensorflow/tfjs-core\": \"^4.2.0\",\n    \"@tensorflow/tfjs-data\": \"^4.2.0\",\n    \"@tensorflow/tfjs-layers\": \"^4.2.0\",\n    \"@tensorflow/tfjs-node\": \"^4.2.0\",\n    \"@tensorflow/tfjs-node-gpu\": \"^4.2.0\",\n    \"@types/node\": \"^18.11.19\",\n    \"@types/offscreencanvas\": \"^2019.7.0\",\n    \"@typescript-eslint/eslint-plugin\": \"^5.51.0\",\n    \"@typescript-eslint/parser\": \"^5.51.0\",\n    \"@vladmandic/build\": \"0.7.15\",\n    \"@vladmandic/pilogger\": \"^0.4.7\",\n    \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n    \"canvas\": \"^2.11.0\",\n    \"esbuild\": \"^0.17.6\",\n    \"eslint\": \"8.33.0\",\n    \"eslint-config-airbnb-base\": \"^15.0.0\",\n    \"eslint-plugin-html\": \"^7.1.0\",\n    \"eslint-plugin-import\": \"^2.27.5\",\n    \"eslint-plugin-json\": \"^3.1.0\",\n    \"eslint-plugin-markdown\": \"^3.0.0\",\n    \"eslint-plugin-node\": \"^11.1.0\",\n    \"eslint-plugin-promise\": \"^6.1.1\",\n    \"rimraf\": \"^4.1.2\",\n    \"tslib\": \"^2.5.0\",\n    \"typedoc\": \"0.23.24\",\n    \"typescript\": \"4.9.5\"\n  }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: null as null | AnyCanvas,\n  gl: null as null | WebGL2RenderingContext,\n  extensions: [] as string[] | null,\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, // default=true\n    desynchronized: true, // default=undefined\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();\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 function register(instance: Human): 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?.getParameter(config.gl.VERSION)) {\n    log('humangl error: backend invalid context');\n    instance.models.reset();\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 = image.canvas(100, 100);\n    } catch (err) {\n      log('humangl error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      if (!config.gl) {\n        log('humangl error: cannot get webgl context');\n        return;\n      }\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('backend 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', (e) => {\n          log('humangl error:', 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        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('humangl error: context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('humangl error: context create:', e);\n        });\n      }\n    } catch (err) {\n      log('humangl error: cannot get webgl context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('humangl error: cannot set webgl context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      // @ts-ignore uncompatible kernelMs timing info\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('humangl 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('humangl error: cannot update webgl backend registration:', err);\n      return;\n    }\n    try {\n      // @ts-ignore private property\n      if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('humangl error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    const backend = tf.backend();\n    const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n    if (current) {\n      if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n    } else {\n      log('humangl error: no current gl context:', current, config.gl);\n    }\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: 255.0,\n  tf1: 1.0,\n  tf2: 2.0,\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(255.0, 'float32');\n  constants.tf1 = tf.scalar(1.0, 'float32');\n  constants.tf2 = tf.scalar(2.0, '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", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise<BackendEnum> {\n  await env.updateBackend(); // update env on backend init\n  if (env.tensorflow?.version) return 'tensorflow';\n  if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n  if (env.webgl.supported && env.webgl.backend) return 'webgl';\n  if (env.wasm.supported && env.wasm.backend) return 'wasm';\n  return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n  const newKernels: string[] = [];\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    newKernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelFloorMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n    };\n    tf.registerKernel(kernelFloorMod);\n    env.kernels.push('floormod');\n    newKernels.push('floormod');\n  }\n  /*\n  if (!env.kernels.includes('atan2') && config.softwareKernels) {\n    const kernelAtan2 = {\n      kernelName: 'Atan2',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => {\n        const backend = tf.getBackend();\n        tf.setBackend('cpu');\n        const t = tf.atan2(op.inputs.a, op.inputs.b);\n        tf.setBackend(backend);\n        return t;\n      }),\n    };\n    if (config.debug) log('registered kernel:', 'atan2');\n    log('registered kernel:', 'atan2');\n    tf.registerKernel(kernelAtan2);\n    env.kernels.push('atan2');\n    newKernels.push('atan2');\n  }\n  */\n  if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n    const kernelRotateWithOffset = {\n      kernelName: 'RotateWithOffset',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => {\n        const backend = tf.getBackend();\n        tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n        const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n        tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n        return t;\n      }),\n    };\n    tf.registerKernel(kernelRotateWithOffset);\n    env.kernels.push('rotatewithoffset');\n    newKernels.push('rotatewithoffset');\n  }\n  if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record<string, unknown> = {};\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\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 = 'webgl';\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 = 'webgl';\n        } else {\n          const adapter = await navigator.gpu.requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n          if (!adapter) {\n            log('override: backend set to webgpu but browser reports no available gpu');\n            instance.config.backend = 'webgl';\n          } else {\n            // @ts-ignore requestAdapterInfo is not in tslib\n            const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n            // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n            log('webgpu adapter info:', adapterInfo);\n          }\n        }\n      }\n\n      // check available backends\n      let available = Object.keys(tf.engine().registryFactory as Record<string, unknown>);\n      if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n        humangl.register(instance);\n        available = Object.keys(tf.engine().registryFactory as Record<string, unknown>);\n      }\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        // @ts-ignore private property\n        if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf.setWasmPaths !== 'undefined') 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        let mt = false;\n        let simd = false;\n        try {\n          mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n          simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\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        } catch {\n          log('wasm detection failed');\n        }\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n      // @ts-ignore private property\n      if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n      // @ts-ignore private property\n      if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false <https://github.com/tensorflow/tfjs/issues/5205>\n      // @ts-ignore private property\n      if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false <https://github.com/tensorflow/tfjs/issues/6678>\n      // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV'])  tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true <https://github.com/tensorflow/tfjs/pull/4909>\n      // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false <https://github.com/tensorflow/tfjs/issues/6687>\n      // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n      // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (instance.config.debug && 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    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    if (instance.config.debug) {\n      // @ts-ignore private property\n      const newFlags = tf.env().flags;\n      const updatedFlags = {};\n      for (const key of Object.keys(newFlags)) {\n        if (defaultFlags[key] === newFlags[key]) continue;\n        updatedFlags[key] = newFlags[key];\n      }\n      if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n    }\n\n    if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n      if (instance.config.debug) log('flags:', instance.config['flags']);\n      for (const [key, val] of Object.entries(instance.config.flags)) {\n        tf.env().set(key, val as number | boolean);\n      }\n    }\n\n    tf.enableProdMode();\n    constants.init();\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend() as BackendEnum;\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps(instance.config);\n    // await env.updateBackend(); // update env on backend init\n    // env.initial = false;\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: 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: (param): TensorInfo => {\n        if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n        return param?.inputs?.info as TensorInfo;\n      },\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 { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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 function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\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?.performance || !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\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n  options.faceLabels = defaultLabels.face;\n  options.bodyLabels = defaultLabels.body;\n  options.bodyPartLabels = defaultLabels.bodyPart;\n  options.handLabels = defaultLabels.hand;\n  options.fingerLabels = defaultLabels.finger;\n  options.objectLabels = defaultLabels.object;\n  options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const 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\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n  const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n  const x = Math.max(0, startX);\n  for (let i = line.length - 1; i >= 0; i--) {\n    const y = i * localOptions.lineHeight + startY;\n    if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n      ctx.fillStyle = localOptions.shadowColor;\n      ctx.fillText(line[i], x + 5, y + 16);\n    }\n    ctx.fillStyle = localOptions.labelColor;\n    ctx.fillText(line[i], x + 4, y + 15);\n  }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  ctx.fillStyle = colorDepth(z, localOptions);\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nexport function 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\nexport function 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    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function 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\nexport function 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", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n  /** draw line color */\n  color: string,\n  /** alpha value used for lines */\n  alpha: number,\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 face attention keypoints be highlighted */\n  drawAttention: 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  /** string template for face labels */\n  faceLabels: string,\n  /** string template for body labels */\n  bodyLabels: string,\n  /** string template for body part labels */\n  bodyPartLabels: string,\n  /** string template for hand labels */\n  handLabels: string,\n  /** string template for hand labels */\n  fingerLabels: string,\n  /** string template for object labels */\n  objectLabels: string,\n  /** string template for gesture labels */\n  gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n  labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n  shadowColor: 'black' as string,\n  alpha: 0.5 as number,\n  font: 'small-caps 16px \"Segoe UI\"' as string,\n  lineHeight: 18 as number,\n  lineWidth: 4 as number,\n  pointSize: 2 as number,\n  roundRect: 8 as number,\n  drawPoints: false as boolean,\n  drawLabels: true as boolean,\n  drawBoxes: true as boolean,\n  drawAttention: true as boolean,\n  drawGestures: true as boolean,\n  drawPolygons: true as boolean,\n  drawGaze: true as boolean,\n  fillPolygons: false as boolean,\n  useDepth: true as boolean,\n  useCurves: false as boolean,\n  faceLabels: '' as string,\n  bodyLabels: '' as string,\n  bodyPartLabels: '' as string,\n  objectLabels: '' as string,\n  handLabels: '' as string,\n  fingerLabels: '' as string,\n  gestureLabels: '' as string,\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], // 11\n  // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n  // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n  // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n  rightEyeIris: [473, 474, 475, 476, 477], // 5\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 irisIndices: { 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] }, // 7 x 3d\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n  { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n  { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\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: 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: 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: 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: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: 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: 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: 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// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const pairsLips: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n  [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n  [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n  [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n  [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n  lips: connectionsToIndices(pairsLips),\n  leftEye: connectionsToIndices(pairsLeftEye),\n  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n  leftIris: connectionsToIndices(pairsLeftIris),\n  rightEye: connectionsToIndices(pairsRightEye),\n  rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n  rightIris: connectionsToIndices(pairsRightIris),\n  faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n  [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n  [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n  [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n  [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n  [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n  [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n  [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n  [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n  [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n  [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n  [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n  [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n  [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n  [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n  [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n  [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n  [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n  [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n  [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n  [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n  [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n  [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n  [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n  [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n  [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n  [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n  [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n  [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n  [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n  [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n  [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n  [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n  [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n  [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n  [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n  [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n  [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n  [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n  [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n  [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n  [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n  [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n  [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n  [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n  [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n  [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n  [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n  [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n  [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n  [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n  [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n  [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n  [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n  [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n  [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n  [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n  [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n  [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n  [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n  [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n  [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n  [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n  [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n  [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 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434], [434, 427], [427, 432],\n  [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n  [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n  [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n  [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n  [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n  [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n  [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n  [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n  [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n  [273, 287], 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426], [426, 322], [322, 436],\n  [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n  [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n  [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n  [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n  [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n  [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n  [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n  [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n  [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n  [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n  [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n  [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n  [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n  [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n  [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n  [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n  [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n  [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], 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466],\n  [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n  [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n  [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n  [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n  [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n  [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n  [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n  [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n  [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n  [5, 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[216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n  [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n  [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n  [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n  [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n  [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n  [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n  [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n  [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n  [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n  [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n  [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n  [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n  [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n  [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n  [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n  [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n  [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n  [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n  [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n  [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n  [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n  [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n  [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n  [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n  [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n  [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n  [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n  [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n  [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n  [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n  [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n  [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n  [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n  [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n  [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n  [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n  [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n  [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n  [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n  [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n  [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n  [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n  [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n  [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n  [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n  [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n  [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n  [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n  [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n  [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n  [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n  [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n  [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n  [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n  [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n  [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n  [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n  [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n  [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n  [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n  [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n  [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n  [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n  [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n  [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n  [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n  [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n  [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n  [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n  [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n  [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n  [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n  [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n  [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n  [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n  [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n  [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n  [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n  [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n  [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n  [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n  [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n  [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n  [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n  [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n  [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n  [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n  [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n  [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n  [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n  [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n  [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n  [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n  [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n  [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n  [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n  [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n  [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n  [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n  [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n  [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n  [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n  [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n  [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n  [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n  [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n  [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n  [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n  [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n  [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n  [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n  [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n  [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n  [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n  [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n  [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n  [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n  [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n  [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n  [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n  [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n  [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n  [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n  [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n  [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n  [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n  [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n  [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n  [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n  [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n  [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n  [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n  [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n  [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n  [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n  [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n  lips: connectionsToIndices(LIPS_CONNECTIONS),\n  leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n  leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n  leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n  rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n  rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n  rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n  faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n  .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n  .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n  indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n  zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n  61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n  185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n  78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n  191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n  76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n  184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n  62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n  183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n  130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n  247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n  226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n  113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n  35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n  143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n  156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n  359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n  467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n  446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n  342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n  265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n  372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n  383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n  468, // Center.\n  469, // Iris right edge.\n  470, // Iris top edge.\n  471, // Iris left edge.\n  472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n  473, // Center.\n  474, // Iris right edge.\n  475, // Iris top edge.\n  476, // Iris left edge.\n  477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n  let l = localOptions.faceLabels.slice();\n  if (f.score) l = replace(l, '[score]', 100 * f.score);\n  if (f.gender) l = replace(l, '[gender]', f.gender);\n  if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n  if (f.age) l = replace(l, '[age]', f.age);\n  if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n  if (f.real) l = replace(l, '[real]', 100 * f.real);\n  if (f.live) l = replace(l, '[live]', 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    l = replace(l, '[emotions]', emotion.join(' '));\n  }\n  if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n  if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n  if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n  if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n  labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (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?.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}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\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}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\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\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (localOptions.drawPolygons && f.mesh.length >= 468) {\n    ctx.lineWidth = 1;\n    for (let i = 0; i < triangulation.length / 3; i++) {\n      const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n      lines(ctx, points, localOptions);\n    }\n    drawIrisElipse(f, ctx);\n  }\n  /*\n  if (localOptions.drawPolygons && f.contours.length > 1) {\n    ctx.lineWidth = 5;\n    lines(ctx, f.contours, opt);\n  }\n  ctx.lineWidth = 1;\n  */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (localOptions.drawPoints) {\n    if (f?.mesh.length >= 468) {\n      for (let i = 0; i < f.mesh.length; i++) {\n        point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n        if (localOptions.drawAttention) {\n          if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n          if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n          if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n        }\n      }\n    } else {\n      for (const [k, v] of Object.entries(f?.annotations || {})) {\n        if (!v?.[0]) continue;\n        const pt = v[0];\n        point(ctx, pt[0], pt[1], 0, localOptions);\n        if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (localOptions.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n  }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial<DrawOptions>) {\n  localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.strokeStyle = localOptions.color;\n  ctx.fillStyle = localOptions.color;\n  for (const f of result) {\n    drawFaceBoxes(f, ctx);\n    drawLabels(f, ctx);\n    if (f.mesh && f.mesh.length > 0) {\n      drawFacePoints(f, ctx);\n      drawFacePolygons(f, ctx);\n      drawGazeSpheres(f, ctx);\n      drawGazeArrows(f, ctx);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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 && (localOptions.bodyLabels?.length > 0)) {\n        let l = localOptions.bodyLabels.slice();\n        l = replace(l, '[score]', 100 * result[i].score);\n        labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\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 = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && 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        let l = localOptions.bodyPartLabels.slice();\n        l = replace(l, '[label]', pt.part);\n        l = replace(l, '[score]', 100 * pt.score);\n        labels(ctx, l, pt.position[0], pt.position[1], localOptions);\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", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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 && (localOptions.handLabels?.length > 0)) {\n        let l = localOptions.handLabels.slice();\n        l = replace(l, '[label]', h.label);\n        l = replace(l, '[score]', 100 * h.score);\n        labels(ctx, l, h.box[0], h.box[1], localOptions);\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 = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n      for (const [part, pt] of Object.entries(h.annotations)) {\n        let l = localOptions.fingerLabels.slice();\n        l = replace(l, '[label]', part);\n        labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n      }\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: 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 = colorDepth(i * z, localOptions);\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", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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 && (localOptions.objectLabels?.length > 0)) {\n        let l = localOptions.objectLabels.slice();\n        l = replace(l, '[label]', h.label);\n        l = replace(l, '[score]', 100 * h.score);\n        labels(ctx, l, h.box[0], h.box[1], localOptions);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n    const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\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      const [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        let l = localOptions.gestureLabels.slice();\n        l = replace(l, '[where]', where[0]);\n        l = replace(l, '[who]', who);\n        l = replace(l, '[what]', what[1]);\n        labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n        i += 1;\n      }\n    }\n  }\n}\n", "export const defaultLabels = {\n  face: `face\n    confidence: [score]%\n    [gender] [genderScore]%\n    age: [age] years\n    distance: [distance]cm\n    real: [real]%\n    live: [live]%\n    [emotions]\n    roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n    gaze: [gaze]\u00B0`,\n  body: 'body [score]%',\n  bodyPart: '[label] [score]%',\n  object: '[label] [score]%',\n  hand: '[label] [score]%',\n  finger: '[label]',\n  gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: 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 { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n  const anchors: { 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\nexport async function loadDetector(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n    model = await loadModel(config.body['detector'].modelPath);\n    const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n  } else if (config.debug && model) log('cached model:', model['modelUrl']);\n  createAnchors();\n  return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\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 xMax = tf.add(xMin, width);\n    const yMax = tf.add(yMin, height);\n    const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n    return boxes;\n  });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const detectedBoxes: DetectedBox[] = [];\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n  const nms = await t.nms.data();\n  const scores = await t.scores.data();\n  const boxes = await t.boxes.array();\n  for (const i of Array.from(nms)) {\n    const score = scores[i];\n    const boxRaw: Box = boxes[i];\n    const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n    const detectedBox: DetectedBox = { score, boxRaw, box };\n    detectedBoxes.push(detectedBox);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = model?.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 decodeResults(t.boxes, t.logits, config, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: 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: 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, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 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\nconst cache: BodyResult[] = [];\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 const loadDetect = (config: Config): Promise<GraphModel> => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    model = await loadModel(config.body.modelPath);\n    const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!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 as Tensor4D, [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 as Tensor4D || 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: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): 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    const width = cropBox[2] - cropBox[0];\n    const height = cropBox[3] - cropBox[1];\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] / width + 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\nfunction fixKeypoints(keypoints: 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  if (!model?.['executor']) return null;\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 */] = model?.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: 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, points[depth * i + 1] / inputSize, 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: 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: 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\nexport async function predict(input: Tensor4D, 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    let boxes: DetectedBox[] = [];\n    if (config.body?.['detector']?.['enabled']) {\n      const preparedImage = prepareImage(input, 224);\n      boxes = await detectBoxes(preparedImage, config, outputSize);\n      tf.dispose(preparedImage);\n    } else {\n      boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n    }\n    for (let i = 0; i < boxes.length; i++) {\n      const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n      cache.length = 0;\n      const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n      tf.dispose(preparedBox);\n      if (!bodyResult) continue;\n      bodyResult.id = i;\n      // bodyResult.score = 0; // TBD\n      cache.push(bodyResult);\n    }\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    lastTime = now();\n    skipped = 0;\n  }\n  return 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: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } 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 = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\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: ObjectResult[] = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1); // 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 as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, 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    if (Number.isNaN(classVal)) continue;\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: Tensor4D, config: Config): Promise<ObjectResult[]> {\n  if (!model?.['executor']) return [];\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: 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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\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, Tensor4D } 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): Promise<[number, number, number]> {\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: number = (await max.data())[0]; // get highest score\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, width);\n    const y: number = (await div.data())[0];\n    tf.dispose([reshaped, max, coordinates, mod, div]);\n    return [x, y, newScore];\n  }\n  tf.dispose([reshaped, max]);\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise<BodyResult[]> {\n  if (!model?.['executor'] || !model?.inputs[0].shape) return [];\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      const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\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 = tf.squeeze(resT);\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = tf.unstack(squeeze, 2);\n      tf.dispose(squeeze);\n\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: 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 * 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, anchor) => {\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  // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];\n  const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);\n  /**\n  face.mesh = box.landmarks.map((pt) => [\n    ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n    ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),\n  ]);\n  */\n\n  return { startPoint, endPoint, 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 {\n    startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n    endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n    landmarks: box.landmarks,\n    confidence: box.confidence,\n    size,\n  };\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 {\n    startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n    endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n    landmarks: box.landmarks,\n    confidence: box.confidence,\n    size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n  };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return {\n    startPoint: [Math.min(...x), Math.min(...y)] as Point,\n    endPoint: [Math.max(...x), Math.max(...y)] as Point,\n    landmarks,\n  };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => 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: 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: 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: number) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: [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')) {\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[0], centerRaw[1]]);\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 * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, 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['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n  if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\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 as Tensor2D, t.endNormalized as Tensor2D], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise<DetectBox[]> {\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  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) && res.length > 2) { // 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 if (Array.isArray(res)) { // new facemesh-detection tfhub model\n    t.batch = tf.squeeze(res[0]);\n  } else { // original blazeface tfhub model\n    t.batch = tf.squeeze(res);\n  }\n  tf.dispose(res);\n  t.boxes = decodeBoxes(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 as Tensor2D, t.scores as Tensor1D, (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: DetectBox[] = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n\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      b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);\n      const anchor = await b.anchor.data();\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) 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", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\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?.['executor'] && 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 and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.irisIndices.length; i++) {\n    const { key, indices } = coords.irisIndices[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],\n          newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\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: 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.\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, meshSize) {\n  if (!model?.['executor']) return rawCoords;\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, false);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceIrisCoords(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 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    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceIrisCoords(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", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n  const t: Record<string, Float32Array> = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n    // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n    // flag: results[1], // already processed in parent // conv_faceflag\n    lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n    irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n    eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n    irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n    eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n  };\n  for (const val of Object.values(t)) {\n    if (!val) return rawCoords; // could not find tensor\n  }\n\n  // augment iris: adds additional 5 keypoints per eye\n  const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n  const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n  // augment eyes: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n  for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n  // augment lips: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n  return rawCoords;\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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\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: Tensor4D, 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: FaceResult[] = [];\n  const newCache: DetectBox[] = [];\n  let id = 0;\n  const size = inputSize;\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      size: [0, 0],\n      // contoursRaw: [],\n      // contours: [],\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 = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n      tf.dispose(face.tensor);\n      if (equilized) face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, 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.size = box.size;\n      face.mesh = box.landmarks;\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\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      if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n        config.face.attention.enabled = false;\n        tf.dispose(face.tensor);\n        return faces;\n      }\n      const results = model.execute(face.tensor as Tensor) as Tensor[];\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const faceConfidence = await confidenceT.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\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        if (config.face.mesh['keepInvalid']) {\n          face.box = util.clampBox(box, input);\n          face.boxRaw = util.getRawBox(box, input);\n          face.score = face.boxScore;\n          face.mesh = box.landmarks;\n          face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n          for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n            face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n          }\n        }\n      } else {\n        const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n        const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n        let rawCoords = await coordsReshaped.array();\n        tf.dispose(coordsReshaped);\n        if (config.face.attention?.enabled) {\n          rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n        } else if (config.face.iris?.enabled) {\n          rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n        }\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) / size]);\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 = {\n          ...util.calculateFaceBox(face.mesh, box),\n          confidence: box.confidence,\n          landmarks: box.landmarks,\n          size: box.size,\n        };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        /*\n        const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n        const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n        face.contoursRaw = [];\n        for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n        face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n        */\n        newCache.push(calculatedBox);\n      }\n      tf.dispose(results);\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 (config.face.attention?.enabled && model?.['signature']) {\n    if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n  }\n  if (!model) {\n    if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n    else model = await loadModel(config.face.mesh?.modelPath);\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    model = await loadModel(config.face.emotion?.modelPath);\n    rgb = model?.inputs?.[0].shape?.[3] === 3;\n    if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n    else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ 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: { 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      if (config.face.emotion['crop'] > 0) { // optional crop\n        const crop = config.face.emotion['crop'];\n        const box = [[crop, crop, 1 - crop, 1 - crop]]; // empyrical values for top, left, bottom, right\n        t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      } else {\n        t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      }\n      if (rgb) {\n        t.mul = tf.mul(t.resize, 255);\n        t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n        t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n      } else {\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      }\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 * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\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 Tensor4D; // 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: Tensor = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm: Tensor = 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: Tensor4D, config: Config, idx: number, count: number): Promise<FaceRes> {\n  const obj: FaceRes = {\n    age: 0 as number,\n    gender: 'unknown' as Gender,\n    genderScore: 0 as number,\n    descriptor: [] as number[],\n  };\n  if (!model?.['executor']) return obj;\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 > 0) && (last?.[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\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 = 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) as Tensor1D, 1);\n      const ageIdx: number = (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[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n      if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\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() : [] as 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 type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * 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: { 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: { 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", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: 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: Tensor4D, config: Config, idx: number, count: number): Promise<number> {\n  if (!model?.['executor']) 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 * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: 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: Tensor4D, config: Config, idx: number, count: number): Promise<number> {\n  if (!model?.['executor']) 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", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: 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\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.gear?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor4D, 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", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\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\nexport async function predict(image: Tensor4D, 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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { 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\nexport async function load(config: Config) {\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\nexport async function predict(image: Tensor4D, 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 * MobileFaceNet 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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: 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: Tensor4D, config: Config, idx, count): Promise<number[]> {\n  if (!model?.['executor']) return [];\n  const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n  const skipTime = (config.face['mobilefacenet']?.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: number[] = [];\n    if (config.face['mobilefacenet']?.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      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: 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['insightface'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise<number[]> {\n  if (!model?.['executor']) return [];\n  const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n  const skipTime = (config.face['insightface']?.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: number[] = [];\n    if (config.face['insightface']?.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      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\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    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\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 (Number.isNaN(thetaX)) thetaX = 0;\n    if (Number.isNaN(thetaY)) thetaY = 0;\n    if (Number.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 yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const zAxis = normalize(crossVectors(xAxis, yAxis));\n  // adjust xAxis to make sure that all axes are perpendicular to each other\n  xAxis = crossVectors(yAxis, zAxis);\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    xAxis[0], xAxis[1], xAxis[2],\n    yAxis[0], yAxis[1], yAxis[2],\n    zAxis[0], zAxis[1], zAxis[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", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n  // iris points are [center, left, top, right, bottom]\n  // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n  const f = face?.annotations;\n  if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n  // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n  const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n  // distance of eye from camera in meters\n  const cameraDistance = Math.round(1.17 / irisSize) / 100;\n  return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n  // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n  const f = face?.annotations;\n  if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n  // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n  const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n  // pixel x and y distance of centers of left and right iris, you can use edges instead\n  const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n  // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n  const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n  // distance between eyes in meters\n  const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n  return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\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 * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface 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: Tensor4D): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\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 insightfaceRes: 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: FaceResult[] = [];\n  instance.state = 'run:face';\n  const faces: FaceResult[] = 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      if (masked) faces[i].tensor = masked;\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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || 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 as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || 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 as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || 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 mobilefacenet alternative, 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 as Tensor4D || 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 as Tensor4D || 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 insightface alternative, inherits face from blazeface\n    instance.analyze('Start InsightFace:');\n    if (instance.config.async) {\n      insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End InsightFace:');\n\n    // run faceres, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\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, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, 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 mobilefacenet model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n      (descRes as DescRes).descriptor = insightfaceRes as number[];\n    }\n\n    const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : 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;\n    if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n    if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n    if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n    if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize > 0) res.distance = irisSize;\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 * 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\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\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\n/* eslint-disable camelcase */\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: number[][] = [];\n  const slopesYZ: number[][] = [];\n  const fingerCurls: number[] = [];\n  const fingerDirections: 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: number[] = [];\n    const slopeAtYZ: 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: { 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 * 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: { 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: { 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: { iris: number, gesture: IrisGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations?.leftEyeIris?.[0] || !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: { hand: number, gesture: HandGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: { 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", "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: 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: 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: 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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, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\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: GraphModel) {\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?.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 as Tensor2D, t.endPoints as Tensor2D], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res as Tensor;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\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] ? this.anchors[index] : 0);\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: Tensor4D, config: 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 as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as number[];\n    const hands: { 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] || 1) / this.inputSize, (input.shape[1] || 0) / 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: ({ 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?.inputs?.[0].shape?.[2] || 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\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: { 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: [number, number] = [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 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 * 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: 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 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 = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n  if (handDetector && handPoseModel) 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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } 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\ninterface HandDetectResult {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: HandDetectResult[],\n  hands: 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 = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\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 = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\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: Tensor4D, 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: 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 as Tensor2D, t.max as Tensor1D, (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: Tensor4D, 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: Tensor4D, config: Config): Promise<HandResult[]> {\n  if (!models[0]?.['executor'] || !models[1]?.['executor'] || !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 * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n  /** face id */\n  id: number\n  /** overall face score */\n  score: number,\n  /** detection score */\n  boxScore: number,\n  /** mesh score */\n  faceScore: number,\n  /** detected face box */\n  box: Box,\n  /** detected face box normalized to 0..1 */\n  boxRaw: Box,\n  /** detected face box size */\n  size: [number, number],\n  /** detected face mesh */\n  mesh: Point[]\n  /** detected face mesh normalized to 0..1 */\n  meshRaw: Point[],\n  /** face contours as array of 2d points normalized to 0..1 */\n  // contoursRaw: Array<[number, number]>,\n  /** face contours as array of 2d points */\n  // contours: Array<[number, number]>,\n  /** mesh keypoints combined into annotated results */\n  annotations: Record<FaceLandmark, Point[]>,\n  /** detected age */\n  age?: number,\n  /** detected gender */\n  gender?: Gender,\n  /** gender detection score */\n  genderScore?: number,\n  /** detected emotions */\n  emotion?: { score: number, emotion: Emotion }[],\n  /** detected race */\n  race?: { score: number, race: Race }[],\n  /** face descriptor */\n  embedding?: number[],\n  /** face distance from camera */\n  distance?: number,\n  /** face anti-spoofing result confidence */\n  real?: number,\n  /** face liveness result confidence */\n  live?: number,\n  /** face rotation details */\n  rotation?: {\n    angle: { roll: number, yaw: number, pitch: number },\n    matrix: [number, number, number, number, number, number, number, number, number],\n    gaze: { bearing: number, strength: number },\n  } | null,\n  /** detected face as tensor that can be used in further pipelines */\n  tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n  | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n  | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n  /** body part name */\n  part: BodyLandmark,\n  /** body part position */\n  position: Point,\n  /** body part position normalized to 0..1 */\n  positionRaw: Point,\n  /** body part position relative to body center in meters */\n  distance?: Point,\n  /** body part detection score */\n  score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n  /** body id */\n  id: number,\n  /** body detection score */\n  score: number,\n  /** detected body box */\n  box: Box,\n  /** detected body box normalized to 0..1 */\n  boxRaw: Box,\n  /** detected body keypoints */\n  keypoints: BodyKeypoint[]\n  /** detected body keypoints combined into annotated parts */\n  annotations: Record<BodyAnnotation, Point[][]>,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n  /** hand id */\n  id: number,\n  /** hand overal score */\n  score: number,\n  /** hand detection score */\n  boxScore: number,\n  /** hand skelton score */\n  fingerScore: number,\n  /** detected hand box */\n  box: Box,\n  /** detected hand box normalized to 0..1 */\n  boxRaw: Box,\n  /** detected hand keypoints */\n  keypoints: Point[],\n  /** detected hand class */\n  label: HandType,\n  /** detected hand keypoints combined into annotated parts */\n  annotations: Record<Finger, Point[]>,\n  /** detected hand parts annotated with part gestures */\n  landmarks: Record<Finger, { curl: FingerCurl, direction: FingerDirection }>,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n  | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n  | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n  | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n  | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n  | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n  /** object id */\n  id: number,\n  /** object detection score */\n  score: number,\n  /** detected object class id */\n  class: number,\n  /** detected object class name */\n  label: ObjectType,\n  /** detected object box */\n  box: Box,\n  /** detected object box normalized to 0..1 */\n  boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n  { 'face': number, gesture: FaceGesture }\n  | { 'iris': number, gesture: IrisGesture }\n  | { 'body': number, gesture: BodyGesture }\n  | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n  /** person id */\n  id: number,\n  /** face result that belongs to this person */\n  face: FaceResult,\n  /** body result that belongs to this person */\n  body: BodyResult | null,\n  /** left and right hand results that belong to this person */\n  hands: { left: HandResult | null, right: HandResult | null },\n  /** detected gestures specific to this person */\n  gestures: GestureResult[],\n  /** box that defines the person */\n  box: Box,\n  /** box that defines the person normalized to 0..1 */\n  boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n  /** {@link FaceResult}: detection & analysis results */\n  face: FaceResult[],\n  /** {@link BodyResult}: detection & analysis results */\n  body: BodyResult[],\n  /** {@link HandResult}: detection & analysis results */\n  hand: HandResult[],\n  /** {@link GestureResult}: detection & analysis results */\n  gesture: GestureResult[],\n  /** {@link ObjectResult}: detection & analysis results */\n  object: ObjectResult[]\n  /** global performance object with timing values for each operation */\n  performance: Record<string, number>,\n  /** optional processed canvas that can be used to draw input on screen */\n  canvas?: AnyCanvas | null,\n  /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n  readonly timestamp: number,\n  /** getter property that returns unified persons object  */\n  persons: PersonResult[],\n  /** Last known error message */\n  error: string | null;\n  /** Resolution width */\n  width: number,\n  /** Resolution height */\n  height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: 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: 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: string[][] = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: 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", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } 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 = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return empty();\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\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  */\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,\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 { 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: 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 }; // 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)); // 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]?.[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      let annotations: Record<FaceLandmark, Point[]> = newResult.face[i].annotations;\n      if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n        bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.face[i].annotations;\n      } else if (newResult.face[i].annotations) {\n        for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n          annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n            ? newResult.face[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\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, annotations }; // shallow clone plus updated values\n      } else {\n        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n      }\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  // copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n  // copy resolution info\n  bufferedResult.width = newResult.width;\n  bufferedResult.height = newResult.height;\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", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\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 predict(input: Tensor4D, config: Config): Promise<Tensor | null> {\n  if (!model) model = await load(config);\n  if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n  const t: Record<string, Tensor> = {};\n  t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n  t.squeeze = tf.squeeze(t.res, [0]);\n  // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n  [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n  // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n  t.fg = tf.softmax(t.fgRaw);\n  t.mul = tf.mul(t.fg, constants.tf255);\n  t.expand = tf.expandDims(t.mul, 2);\n  t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n  let rgba: Tensor;\n  switch (config.segmentation.mode || 'default') {\n    case 'default':\n      t.input = tf.squeeze(input);\n      t.concat = tf.concat([t.input, t.output], -1);\n      rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n      break;\n    case 'alpha':\n      rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n      break;\n    default:\n      rgba = tf.tensor(0);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = 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  if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\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 `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: 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) { // 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 = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\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 * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\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 facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n  if (!model) return null;\n  if (!instance?.config?.validateModels) return null;\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n  const ops: string[] = [];\n  const missing: string[] = [];\n  interface Op { name: string, category: string, op: string }\n  const url = model['modelUrl'] as string;\n  const executor = model['executor'];\n  if (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) {\n      log('model not loaded', name);\n    }\n  }\n  for (const op of ops) {\n    if (!simpleOps.includes(op) // exclude simple ops\n      && !ignoreOps.includes(op) // exclude specific 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  if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n  return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n  numLoadedModels: number,\n  numDefinedModels: number,\n  percentageLoaded: number,\n  totalSizeFromManifest: number,\n  totalSizeWeights: number,\n  totalSizeLoading: number,\n  modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n  private instance: Human;\n  models: Record<string, null | GraphModel> = {};\n\n  constructor(currentInstance: Human) {\n    this.models = {};\n    this.instance = currentInstance;\n  }\n\n  stats(): ModelStats {\n    let totalSizeFromManifest = 0;\n    let totalSizeWeights = 0;\n    let totalSizeLoading = 0;\n    for (const m of Object.values(modelStats)) {\n      totalSizeFromManifest += m.sizeFromManifest;\n      totalSizeWeights += m.sizeLoadedWeights;\n      totalSizeLoading += m.sizeDesired;\n    }\n    const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n    return {\n      numLoadedModels: Object.values(modelStats).length,\n      numDefinedModels: Object.keys(this.models).length,\n      percentageLoaded,\n      totalSizeFromManifest,\n      totalSizeWeights,\n      totalSizeLoading,\n      modelStats: Object.values(modelStats),\n    };\n  }\n\n  reset(): void {\n    for (const model of Object.keys(this.models)) this.models[model] = null;\n  }\n\n  async load(instance?: Human): Promise<void> {\n    if (env.initial) this.reset();\n    if (instance) this.instance = instance;\n    const m: Record<string, null | GraphModel | Promise<GraphModel>> = {};\n    // face main models\n    m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n    m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n    m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n    m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n    m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n    m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n    m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n    // face alternatives\n    m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n    m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n    m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n    m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n    m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n    // body alterinatives\n    m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n    m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n    m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n    m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n    m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n    // hand alternatives\n    m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n    m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n    if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n    // object detection alternatives\n    m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n    m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n    // segmentation alternatives\n    m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n    m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n    m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n    // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n    for (const [model, promise] of Object.entries(m)) {\n      if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n    }\n    await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n  }\n\n  list() {\n    const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n    for (const m of models) {\n      const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n      if (!stats) continue;\n      m.size = modelStats[stats].sizeLoadedWeights;\n      m.url = modelStats[stats].url;\n    }\n    return models;\n  }\n\n  loaded() {\n    const list = this.list();\n    const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n    return loaded;\n  }\n\n  validate(): { name: string, missing: string[] }[] {\n    const missing: KernelOps[] = [];\n    for (const defined of Object.keys(this.models)) {\n      const model: GraphModel | null = this.models[defined as keyof Models];\n      if (!model) continue;\n      const res = validateModel(this.instance, model, defined);\n      if (res) missing.push(res);\n    }\n    return missing;\n  }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: 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: BodyKeypoint[]): 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?.[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 as Tensor3D, [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?.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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\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: Box[], // unused\n  bodies: 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?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: 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: 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: 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\nfunction parseMultiPose(res, config, image) {\n  const bodies: 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: 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: 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?.['executor'] || !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      ? parseSinglePose(res, config, input)\n      : 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: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\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, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: 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 = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\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: ObjectResult[] = [];\n  const size = inputSize;\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    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) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n    const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n    const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n    const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n    const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n    const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n    for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n      for (let j = 0; j < (scoresT.shape?.[1] || 0); 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 / (size))); // 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    tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n  }\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: number[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = Array.from(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: Tensor4D, config: Config): Promise<ObjectResult[]> {\n  if (!model?.['executor']) return [];\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 resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const normT = tf.div(resizeT, constants.tf255);\n    const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transposeT);\n    lastTime = now();\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    tf.dispose([resizeT, normT, transposeT, ...objectT]);\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]): 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: 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: number, 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: number, 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: { x: number, y: number }, b: { x: number, y: number }) {\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 * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } 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: number, 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: number, heatmapY: number, heatmapX: number, scores) {\n  const [height, width]: [number, number] = 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: { 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: Tensor4D, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  if (!model?.['executor']) return [];\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: Tensor[] = model.execute(normalized, poseNetOutputs) as 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 = 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]]);\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 * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record<string, Tensor> = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n  tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n  t.r1i = tf.tensor(0.0);\n  t.r2i = tf.tensor(0.0);\n  t.r3i = tf.tensor(0.0);\n  t.r4i = tf.tensor(0.0);\n  ratio = config.segmentation.ratio || 0.5;\n  t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\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  init(config);\n  return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n  const squeeze = tf.squeeze(r, ([0]));\n  const mul = tf.mul(squeeze, constants.tf255);\n  const cast = tf.cast(mul, 'int32');\n  return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n  const rgb = fgr\n    ? normalize(fgr) // normalize and use value\n    : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n  const a = pha\n    ? normalize(pha) // normalize and use value\n    : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n  const rgba = tf.concat([rgb, a], -1);\n  tf.dispose([rgb, a]);\n  return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n  return tf.tidy(() => {\n    const r: Record<string, Tensor | Tensor[]> = {};\n    r.unstack = tf.unstack(state, -1);\n    r.concat = tf.concat(r.unstack, 1);\n    r.split = tf.split(r.concat, 4, 1);\n    r.stack = tf.concat(r.split, 2);\n    r.squeeze = tf.squeeze(r.stack, [0]);\n    r.expand = tf.expandDims(r.squeeze, -1);\n    r.add = tf.add(r.expand, 1);\n    r.mul = tf.mul(r.add, 127.5);\n    r.cast = tf.cast(r.mul, 'int32');\n    r.tile = tf.tile(r.cast, [1, 1, 3]);\n    r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n    return tf.concat([r.tile, r.alpha], -1);\n  });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise<Tensor | null> {\n  if (!model) model = await load(config);\n  if (!model?.['executor']) return null;\n  // const expand = tf.expandDims(input, 0);\n  t.src = tf.div(input, 255);\n  if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n  const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n  let rgba: Tensor;\n  switch (config.segmentation.mode || 'default') {\n    case 'default':\n      rgba = getRGBA(fgr, pha);\n      break;\n    case 'alpha':\n      rgba = getRGBA(null, pha);\n      break;\n    case 'foreground':\n      rgba = getRGBA(fgr, null);\n      break;\n    case 'state':\n      rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n      break;\n    default:\n      rgba = tf.tensor(0);\n  }\n  tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n  return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\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 predict(input: Tensor4D, config: Config): Promise<Tensor | null> {\n  if (!model) model = await load(config);\n  if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n  const t: Record<string, Tensor> = {};\n  t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n  t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n  t.mul = tf.mul(t.alpha, constants.tf255);\n  let rgba: Tensor;\n  switch (config.segmentation.mode || 'default') {\n    case 'default':\n      t.input = tf.squeeze(input);\n      t.concat = tf.concat([t.input, t.mul], -1);\n      rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n      break;\n    case 'alpha':\n      rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n      break;\n    default:\n      rgba = tf.tensor(0);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return rgba;\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: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n  let id = 0;\n  const persons: 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?.[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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob: Blob | null;\n  let res: Result | undefined;\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: string;\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 = '';\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 {\n      resolve(undefined);\n      return;\n    }\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') as CanvasRenderingContext2D;\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, true);\n        const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\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: Result;\n  if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n    // @ts-ignore\n    const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n    const expanded: Tensor = tf.expandDims(data, 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  // @ts-ignore\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\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  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n  // @ts-ignore private property\n  if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n    // log('compile pass: skip');\n    return;\n  }\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  const compiledModels: string[] = [];\n  for (const [modelName, model] of Object.entries(instance.models.models)) {\n    if (!model) continue;\n    const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    try {\n      const res = model.execute(tensor);\n      compiledModels.push(modelName);\n      if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n      else tf.dispose(res);\n    } catch {\n      if (instance.config.debug) log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend['checkCompileCompletionAsync']();\n  webGLBackend['getUniformLocations']();\n  if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  await backend.check(instance, false);\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 empty();\n  }\n  return new Promise(async (resolve) => {\n    await instance.models.load();\n    await runCompile(instance);\n    const res = await runInference(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 * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\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 rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\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](https://js.tensorflow.org/api/latest/)\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: 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  draw: typeof draw = draw;\n\n  /** Face Matching\n   * - similarity: compare two face descriptors and return similarity index\n   * - distance: compare two face descriptors and return raw calculated differences\n   * - find: compare face descriptor to array of face descriptors and return best match\n   */\n  match: typeof match = match;\n\n  /** Currently loaded models\n   * @internal\n   * {@link models#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  // 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    /*\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    */\n    const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n    defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\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    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\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(this);\n    // reexport draw methods\n    draw.init();\n    this.result = empty();\n    // export access to image processing\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    // init model validation\n    models.validateModel(this, null, '');\n    // include platform info\n    this.emit('create');\n    if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n    if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n    const envTemp = JSON.parse(JSON.stringify(this.env));\n    delete envTemp.kernels;\n    delete envTemp.initial;\n    delete envTemp.perfadd;\n    if (this.config.debug) log('environment:', envTemp);\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    image.reset();\n    env.initial = true;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    const msgs = validate(defaults, userConfig || this.config);\n    if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n    return msgs;\n  }\n\n  /** Utility wrapper for performance.now() */\n  now(): number { // eslint-disable-line class-methods-use-this\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 = false) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns RGBA tensor\n   * Note: Segmentation is not triggered as part of detect process\n   *\n   * @param input - {@link Input}\n   * Returns tensor which contains image data in RGBA format\n   */\n  async segmentation(input: Input, userConfig?: Partial<Config>): Promise<Tensor | null> {\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n    if (!this.config.segmentation.enabled) return null;\n    const processed = await image.process(input, this.config);\n    if (!processed.tensor) return null;\n    let tensor: Tensor | null = null;\n    if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n    if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n    if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n    tf.dispose(processed.tensor);\n    return tensor;\n  }\n\n  /** Compare two input tensors for pixel similarity\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    image.reset();\n  }\n\n  /** WebCam helper methods\n   *\n   */\n  public webcam = new WebCam();\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.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (!await backend.check(this, false)) 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('tf flags:', this.tf.ENV.flags);\n      }\n    }\n\n    await this.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.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      this.models.validate(); // 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 || 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?.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);\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<{ kernel: string, time: number, perc: number }[]> {\n    // @ts-ignore profile wraps method return values\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    let total = 0;\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      const ms = Number(kernel.kernelTimeMs) || 0;\n      if (kernels[kernel.name]) kernels[kernel.name] += ms;\n      else kernels[kernel.name] = ms;\n      total += ms;\n    }\n    const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n    for (const kernel of kernelArr) {\n      kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n      kernel.time = Math.round(1000 * kernel.time) / 1000;\n    }\n    kernelArr.sort((a, b) => b.time - a.time); // sort\n    kernelArr.length = 20; // crop\n    return kernelArr;\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(empty(error));\n      }\n\n      const timeStart = now();\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: Tensor4D };\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(empty('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      this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames)++;\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 || [0, 0, 0, 0];\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        width: shape[2],\n        height: shape[1],\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  /** Helper function\n   * @param ms - sleep time in miliseconds\n   */\n  async sleep(ms: number): Promise<void> { // eslint-disable-line class-methods-use-this\n    return new Promise((resolve) => { setTimeout(resolve, ms); });\n  }\n\n  /** internal structure that keeps track of processed videos @hidden */\n  #loops: Record<string, boolean> = {};\n  /** Continously detect video frames\n   * @param element - HTMLVideoElement input\n   * @param run - boolean run continously or stop if already running, default true\n   * @param delay - number delay detection between frames for number of miliseconds, default 0\n   */\n  async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n    if (run) {\n      if (!this.#loops[element.id]) {\n        if (this.config.debug) log('video start', element.id);\n        this.#loops[element.id] = true;\n      }\n      if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n      if (delay > 0) await this.sleep(delay);\n      if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n    } else {\n      if (this.config.debug) log('video stop', element.id);\n      this.#loops[element.id] = false;\n    }\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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 }
diff --git a/dist/human.js b/dist/human.js
index ba54b346..34d1620b 100644
--- a/dist/human.js
+++ b/dist/human.js
@@ -4,57 +4,57 @@
   author: <https://github.com/vladmandic>'
 */
 
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n=++this.pendingBackendInitId,r=a.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,zr(`Initialization of backend ${e} failed`),zr(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=a,{success:!0,asyncInit:!1}}catch(a){return zr(`Initialization of backend ${e} failed`),zr(a.stack||a.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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 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n=this.backend.numDataIds(),r=0;a.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,a=[],n=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Rm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Rm(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Ec(h,this.backendName);F(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let x=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,x);let A=x.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(n){let b=this.getTensorsForGradient(h,f,A);a=this.saveTensorsForBackwardMode(b)}return A}}else{let{forwardFunc:h}=e,f=m=>{n&&(a=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(l,m,g),g}}let{inputs:u,attrs:p}=e,c=Rm(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(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(l,u,t,c,a,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[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(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,a){let n=Wm(e);if(n!=null){let r=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=a.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,a,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");a=a||"float32",n=n||this.backend;let r=e;a==="string"&&Lr(e[0])&&(r=e.map(o=>Xd(o)));let s=n.write(r,t,a),i=new pt(t,a,s,this.nextTensorId());if(this.trackTensor(i,n),a==="string"){let o=this.state.tensorInfo.get(s),l=ux(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,a,n){a=a||"float32";let r={dataId:e,shape:t,dtype:a};return this.makeTensorFromTensorInfo(r,n)}makeTensorFromTensorInfo(e,t){let{dataId:a,shape:n,dtype:r}=e,s=new pt(n,r,a,this.nextTensorId());return this.trackTensor(s,t),s}makeVariable(e,t=!0,a,n){a=a||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let r=new od(e,t,a,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return 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n=this.backend.numDataIds(),r=0;a.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,a=[],n=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Rm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Rm(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Nc(h,this.backendName);F(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let x=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,x);let A=x.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(n){let b=this.getTensorsForGradient(h,f,A);a=this.saveTensorsForBackwardMode(b)}return A}}else{let{forwardFunc:h}=e,f=m=>{n&&(a=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(l,m,g),g}}let{inputs:u,attrs:p}=e,c=Rm(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(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(l,u,t,c,a,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[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(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,a){let n=Wm(e);if(n!=null){let r=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=a.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,a,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");a=a||"float32",n=n||this.backend;let r=e;a==="string"&&Lr(e[0])&&(r=e.map(o=>qd(o)));let s=n.write(r,t,a),i=new pt(t,a,s,this.nextTensorId());if(this.trackTensor(i,n),a==="string"){let o=this.state.tensorInfo.get(s),l=lx(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,a,n){a=a||"float32";let r={dataId:e,shape:t,dtype:a};return this.makeTensorFromTensorInfo(r,n)}makeTensorFromTensorInfo(e,t){let{dataId:a,shape:n,dtype:r}=e,s=new pt(n,r,a,this.nextTensorId());return this.trackTensor(s,t),s}makeVariable(e,t=!0,a,n){a=a||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let r=new id(e,t,a,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 a=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(a=e.size*Tc(e.dtype)),this.state.numBytes+=a,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:a})),e instanceof id||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 a=e.size*Tc(e.dtype);this.state.numBytes-=a}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,a=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-a;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,a,n,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:a,saved:r},o=Wm(e);o!=null&&(n=o.gradFunc),n!=null&&(i.gradient=l=>(l=l.map((u,p)=>{if(u==null){let c=a[p],d=jc(c.size,c.dtype);return this.makeTensor(d,c.shape,c.dtype)}return u}),n(l.length>1?l:l[0],r,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=X2(e),a=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!a.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(r=>{!r.kept&&r.scopeId===n.id&&this.track(r)})}gradients(e,t,a,n=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),a!=null&&a.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${a.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(r instanceof pt,()=>"The result y returned by f() must be a tensor.");let s=qS(this.state.activeTape,t,r);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[r.id]=a==null?sT(r.shape):a,XS(i,s,l=>this.tidy(l),iT);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:r,grads:o}})}customGrad(e){return F(Hr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof pt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let a,n={};t.forEach((i,o)=>{n[o]=i});let r=(i,o)=>(a=e(...t,o),F(a.value instanceof pt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(Hr(a.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),a.value),s=(i,o)=>{let l=a.gradFunc(i,o),u=Array.isArray(l)?l:[l];F(u.length===t.length,()=>"The function f passed in customGrad(f) must 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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)}},Th=class extends os{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}static get className(){return"SGD"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=L.registeredVariables[t];Fe(()=>{let s=be(te(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=On(ze(-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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_A(e,t)}dispose(){this.iterations_!=null&&J(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ze(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(os,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var j1=class extends os{constructor(e,t,a=null){super(),this.learningRate=e,this.rho=t,this.epsilon=a,this.accumulatedGrads=[],this.accumulatedUpdates=[],a==null&&(this.epsilon=L.backend.epsilon())}static get className(){return"Adadelta"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t],r=!1;this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accum_grad`,variable:Oe(()=>Za(n).variable(r))}),this.accumulatedUpdates[a]==null&&(this.accumulatedUpdates[a]={originalName:`${t}/accum_var`,variable:Oe(()=>Za(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[a].variable,o=this.accumulatedUpdates[a].variable;Oe(()=>{let l=be(te(i,this.rho),te(Sn(s),1-this.rho)),u=te(xe(Jn(be(o,this.epsilon)),Jn(be(i,this.epsilon))),s),p=be(te(o,this.rho),te(Sn(u),1-this.rho));i.assign(l),o.assign(p);let c=be(te(u,-this.learningRate),n);n.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(J(this.accumulatedGrads.map(e=>e.variable)),J(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,a=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},q1=class extends os{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}static get className(){return"Adagrad"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accumulator`,variable:Oe(()=>nr(n.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[a].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[a].variable;Oe(()=>{let i=be(s,Sn(r));s.assign(i);let o=be(te(xe(r,Jn(be(i,L.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&J(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(a=>({originalName:a.name,variable:a.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},X1=class extends os{constructor(e,t,a,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Oe(()=>{this.accBeta1=ze(t).variable(),this.accBeta2=ze(a).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}static get className(){return"Adam"}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);Oe(()=>{let a=ge(1,this.accBeta1),n=ge(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Oe(()=>Za(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:Oe(()=>Za(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,c=be(te(u,this.beta1),te(l,1-this.beta1)),d=be(te(p,this.beta2),te(Sn(l),1-this.beta2)),h=xe(c,a),f=xe(d,n);u.assign(c),p.assign(d);let m=be(te(xe(h,be(Jn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(te(this.accBeta1,this.beta1)),this.accBeta2.assign(te(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&J(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),Oe(()=>{this.accBeta1.assign(ll(this.beta1,this.iterations_+1)),this.accBeta2.assign(ll(this.beta2,this.iterations_+1))});let t=e.length/2,a=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}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)}},K1=class extends os{constructor(e,t,a,n=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Oe(()=>{this.iteration=ze(0).variable(),this.accBeta1=ze(t).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}static get className(){return"Adamax"}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);Oe(()=>{let a=ge(1,this.accBeta1),n=xe(-this.learningRate,be(te(this.iteration,this.decay),1));t.forEach((r,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)}},Sh=class extends os{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}static get className(){return"SGD"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=L.registeredVariables[t];Oe(()=>{let s=be(te(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=On(ze(-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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implemented`)}},pF=(e,t,a,n=Zt)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=w("n",e,t,a),s=w("axis",e,t,a),i=w("tensors",e,t,a);return i=i.slice(0,r),[n.concat(i,s)]}case"Gather":{let r=w("x",e,t,a),s=w("indices",e,t,a);return[n.gather(r,n.cast(s,"int32"),0)]}case"GatherV2":{let r=w("axis",e,t,a),s=w("batchDims",e,t,a),i=w("x",e,t,a),o=w("indices",e,t,a);return[n.gather(i,n.cast(o,"int32"),r,s)]}case"Reverse":{let r=w("dims",e,t,a),s=[];for(let o=0;o<r.length;o++)r[o]&&s.push(o);let i=w("x",e,t,a);return[n.reverse(i,s)]}case"ReverseV2":{let r=w("axis",e,t,a),s=w("x",e,t,a);return[n.reverse(s,r)]}case"Slice":{let r=w("begin",e,t,a),s=w("size",e,t,a);return[n.slice(w("x",e,t,a),r,s)]}case"StridedSlice":{let r=w("begin",e,t,a),s=w("end",e,t,a),i=w("strides",e,t,a),o=w("beginMask",e,t,a),l=w("endMask",e,t,a),u=w("ellipsisMask",e,t,a),p=w("newAxisMask",e,t,a),c=w("shrinkAxisMask",e,t,a),d=w("x",e,t,a);return[n.stridedSlice(d,r,s,i,o,l,u,p,c)]}case"Pack":return Fe(()=>{let r=w("axis",e,t,a),s=w("tensors",e,t,a),i=s[0].shape,o=n.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(n.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:n.reshape(u,i)});return[n.stack(l,r)]});case"Unpack":{let r=w("axis",e,t,a),s=w("tensor",e,t,a);return n.unstack(s,r)}case"Tile":{let r=w("reps",e,t,a);return[n.tile(w("x",e,t,a),r)]}case"Split":case"SplitV":{let r=w("axis",e,t,a),s=w("numOrSizeSplits",e,t,a),i=w("x",e,t,a);return n.split(i,s,r)}case"ScatterNd":{let r=w("indices",e,t,a),s=w("values",e,t,a),i=w("shape",e,t,a);return[n.scatterND(r,s,i)]}case"GatherNd":{let r=w("x",e,t,a),s=w("indices",e,t,a);return[n.gatherND(r,s)]}case"SparseToDense":{let r=w("sparseIndices",e,t,a),s=w("outputShape",e,t,a),i=w("sparseValues",e,t,a),o=w("defaultValue",e,t,a);return[n.sparseToDense(r,i,s,i.dtype===o.dtype?o:n.cast(o,i.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},cF=(e,t,a,n=Zt)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=n.sparse.sparseFillEmptyRows(w("indices",e,t,a),w("values",e,t,a),w("denseShape",e,t,a),w("defaultValue",e,t,a));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=n.sparse.sparseReshape(w("inputIndices",e,t,a),w("inputShape",e,t,a),w("newShape",e,t,a));return[r,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(w("data",e,t,a),w("indices",e,t,a),w("segmentIds",e,t,a))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(w("data",e,t,a),w("indices",e,t,a),w("segmentIds",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hF=(e,t,a,n=Zt)=>{switch(e.op){case"FFT":return[n.fft(w("x",e,t,a))];case"IFFT":return[n.ifft(w("x",e,t,a))];case"RFFT":return[n.rfft(w("x",e,t,a))];case"IRFFT":return[n.irfft(w("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},fF=(e,t,a,n=Zt)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=n.string.stringNGrams(w("data",e,t,a),w("dataSplits",e,t,a),w("separator",e,t,a),w("nGramWidths",e,t,a),w("leftPad",e,t,a),w("rightPad",e,t,a),w("padWidth",e,t,a),w("preserveShortSequences",e,t,a));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=n.string.stringSplit(w("input",e,t,a),w("delimiter",e,t,a),w("skipEmpty",e,t,a));return[r,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(w("input",e,t,a),w("numBuckets",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mF=(e,t,a,n=Zt)=>{switch(e.op){case"Cast":return[n.cast(w("x",e,t,a),w("dtype",e,t,a))];case"ExpandDims":{let r=w("axis",e,t,a);return[n.expandDims(w("x",e,t,a),r)]}case"Squeeze":{let r=w("axis",e,t,a);return[n.squeeze(w("x",e,t,a),r)]}case"Reshape":return[n.reshape(w("x",e,t,a),w("shape",e,t,a))];case"MirrorPad":return[n.mirrorPad(w("x",e,t,a),w("padding",e,t,a),w("mode",e,t,a))];case"PadV2":case"Pad":return[n.pad(w("x",e,t,a),w("padding",e,t,a),w("constantValue",e,t,a))];case"SpaceToBatchND":{let r=w("blockShape",e,t,a),s=w("paddings",e,t,a);return[n.spaceToBatchND(w("x",e,t,a),r,s)]}case"BatchToSpaceND":{let r=w("blockShape",e,t,a),s=w("crops",e,t,a);return[n.batchToSpaceND(w("x",e,t,a),r,s)]}case"DepthToSpace":{let r=w("blockSize",e,t,a),s=w("dataFormat",e,t,a).toUpperCase();return[n.depthToSpace(w("x",e,t,a),r,s)]}case"BroadcastTo":return[n.broadcastTo(w("x",e,t,a),w("shape",e,t,a))];case"BroadcastArgs":return[n.broadcastArgs(w("s0",e,t,a),w("s1",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ty(e,t,a,n,r=Fe){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>GP(i,o,l));case"basic_math":return r(()=>HP(i,o,l));case"control":return YP(i,o,l);case"convolution":return r(()=>JP(i,o,l));case"creation":return r(()=>QP(i,o,l));case"dynamic":return eF(i,o,l);case"evaluation":return r(()=>tF(i,o,l));case"image":return r(()=>sF(i,o,l));case"graph":return r(()=>aF(i,o,l));case"logical":return r(()=>iF(i,o,l));case"matrices":return r(()=>oF(i,o,l));case"normalization":return r(()=>lF(i,o,l));case"ragged":return r(()=>uF(i,o,l));case"reduction":return r(()=>dF(i,o,l));case"slice_join":return r(()=>pF(i,o,l));case"sparse":return r(()=>cF(i,o,l));case"spectral":return r(()=>hF(i,o,l));case"string":return r(()=>fF(i,o,l));case"transformation":return r(()=>mF(i,o,l));case"hash_table":return rF(i,o,l,n);case"custom":let u=k4(i.op);if(u&&u.customExecutor)return u.customExecutor(new UP(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. 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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 ny(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>qa(d)[0]),p=[];n!=null&&(p=n.map(d=>qa(d.name)[0]));let c=[...t];for(;c.length>0;){let d=c.pop();if((G4(d)||bF(d)||vF(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&u.indexOf(d.name)===-1&&p.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function gF(e,t,a){let{usedNodes:n,inputs:r}=a,s=[],i=Object.keys(r).map(p=>qa(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{n.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{n.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{n.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(c=>{!l.has(c.name)&&n.has(c.name)&&c.inputs.every(d=>l.has(d.name))&&s.push(c)})}return u}var yF=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],xF=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],AF=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function G4(e){return yF.indexOf(e.op)>=0}function bF(e){return xF.indexOf(e.op)>=0}function vF(e){return AF.indexOf(e.op)>=0}var f2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(a=>{this._functionExecutorMap[a]=new f2(e.functions[a],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(a=>e[a].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 a=e.map(r=>r.name).sort(),n=t.map(r=>r.name).sort();return a.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let a=ny(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:r,syncInputs:s}=a;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. 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Missing the following inputs: [${n}]`)}return gF(this.graph,this.weightMap,a)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return On(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,a])=>[t,this.cloneTensorList(a)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let a=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=a.map(p=>this.graph.nodes[qa(p)[0]]),r=t.map(p=>qa(p)[0]),s=r.map(p=>this.graph.nodes[p]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));try{this.keepIntermediateTensors=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},u={};return Fe(()=>{let p=new ay(this.weightMap,l,u,this.functionExecutorMap),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(f=>{let[m,g]=qa(f),y=[];y[g]=e[f],c[m]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(y))});let d=this.getFrozenTensorIds(c),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!c[m.name]){let g=ty(m,c,p,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);c[m.name]=g,this.keepIntermediateTensors&&(this.clonedTensorsMap[m.name]=this.cloneTensorList(g)),this.checkTensorForDisposal(m.name,m,c,p,d,r,h)}}return this.parent==null&&p.dispose(d),t.map(f=>va(f,c,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(a=>e[a]).map(a=>a.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,a,n,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(a[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=kP(o.name,a,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,a=!1,n={},r={}){this.disposeIntermediateTensors(),a||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let s=new ay(this.weightMap,n,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,a),o=t.map(c=>va(c,i,s)),l=o.map(c=>c.id),u=Object.keys(e).map(c=>e[c].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(c=>{c.forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,a){let n=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(n,this.outputNodes,!0,t,a)}async executeWithControlFlow(e,t,a,n){let r=Object.keys(e),s=r.map(x=>this.graph.nodes[qa(x)[0]]),i=a.map(x=>qa(x)[0]),o=i.map(x=>this.graph.nodes[x]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:c}=ny(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(x=>{let[A,b]=qa(x),k=[];k[b]=e[x],h[A]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let x=this.processStack(s,d,t,h,g,m,i,f,l);await Promise.all(x)}p==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(x=>!G4(x)&&!va(x.name,h,t)).map(x=>x.name);if(y.length>0){let x="";throw p!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${x}`)}return h}processStack(e,t,a,n,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();a.currentContext=p.contexts;let c="";if(p.node.op==="Enter"&&w("isConstant",p.node,n,a)&&([c]=gr(p.node.name,a)),n[p.node.name]==null){let d=ty(p.node,n,a,this._resourceManager);c||([c]=gr(p.node.name,a));let h=a.currentContext;v.isPromise(d)?u.push(d.then(f=>(n[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=h,this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l),f))):(n[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l))}else this.processChildNodes(p.node,t,a,n,r,l)}return u}processChildNodes(e,t,a,n,r,s){e.children.forEach(i=>{let[o]=gr(i.name,a);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!va(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})):i.inputNames.every(l=>!!va(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let a=e[t],[n]=qa(t),r=this.graph.nodes[n];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===a.shape.length&&a.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${a.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(a.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(e){var t,a;let n={};for(let r in e){let s=(a=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||a===void 0?void 0:a[r];s!=null?n[s.name]=e[r]:n[r]=e[r]}return n}checkInputs(e){let t=Object.keys(e).filter(a=>{let[n]=qa(a);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var a,n;let r=(n=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||n===void 0?void 0:n[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[a]=qa(t);if(!this.graph.nodes[a])throw new Error(`The output '${t}' is not found in the graph`)})}},kF=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},wF="?tfjs-format=file",IF="model.json",dp=class{constructor(e,t={},a=jn){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=a,t==null&&(this.loadOptions={}),this.resourceManager=new kF}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=a,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new f2(Y5.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Y5.Instance.transformGraph(e.modelInitializer);this.initializer=new f2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof pt?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof pt)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let a=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+a!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-a} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[n++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,a=Object.keys(t);for(let n=0;n<a.length;n++){let r=a[n],s=t[r];this.resourceIdToCapturedInput[s.resourceId]=e[n]}}}execute(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let a=this.executor.execute(e,t);return a.length>1?a:a[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let a=await this.executor.executeAsync(e,t);return a.length>1?a:a[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,a)=>(t[a]=[e[a]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&J(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function s3(e,t={},a=jn){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&&typeof e=="string"&&(e=TF(e));let n=new dp(e,t,a);return await n.load(),n}function SF(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[n,r]=e;if(!n)throw new Error("modelJSON must be the first element of the array");if(!r||!(r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");let s=jn.getWeightSpecs(n.weightsManifest),i=jn.getModelArtifactsForJSONSync(n,s,r);t=jn.fromMemorySync(i)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=jn.fromMemorySync(e);else throw new Error("Unknown model format");let a=new dp(t);return a.load(),a}function TF(e){return e.endsWith("/")||(e=e+"/"),`${e}${IF}${wF}`}var CF="4.2.0";function Ae(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var NF=Cn.whereImpl,Ch=class extends Al{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new wd(this,vt())}nextDataId(){return Ch.nextDataId++}write(e,t,a){this.firstUse&&(this.firstUse=!1,V().get("IS_NODE")&&T.warn(`
+        ${n}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=h2(s,a),o=n===0?0:e.size/n,l=Oe(()=>{let p=[];e=Q(e,[1,n,o]);for(let c=0;c<t.length;++c){let d=[0,c===0?0:r[c-1],0],h=[1,t[c],o];p[c]=Q(Fe(e,d,h),i)}return e.dispose(),p}),u=new hl([],a,e.dtype,t.length);for(let p=0;p<l.length;p++)u.setItem(p,l[p]);return u}var YP=async(e,t,a)=>{switch(e.op){case"If":case"StatelessIf":{let n=w("thenBranch",e,t,a),r=w("elseBranch",e,t,a),s=w("cond",e,t,a),i=w("args",e,t,a);return(await s.data())[0]?a.functionMap[n].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap):a.functionMap[r].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap)}case"While":case"StatelessWhile":{let n=w("body",e,t,a),r=w("cond",e,t,a),s=w("args",e,t,a),i=await a.functionMap[r].executeFunctionAsync(s,a.tensorArrayMap,a.tensorListMap),o=s.map(p=>p.id),l=await i[0].data();i.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&p.dispose()});let u=s;for(;l[0];){let p=u;u=await 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n=w("size",e,t,a),r=w("dtype",e,t,a),s=w("elementShape",e,t,a),i=w("dynamicSize",e,t,a),o=w("clearAfterRead",e,t,a),l=w("identicalElementShapes",e,t,a),u=w("name",e,t,a),p=new jP(u,r,n,s,l,i,o);return a.addTensorArray(p),[p.idTensor,ze(1)]}case"TensorArrayWriteV3":{let n=w("tensorArrayId",e,t,a),r=w("index",e,t,a),s=w("tensor",e,t,a),i=a.getTensorArray(n.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let n=w("tensorArrayId",e,t,a),r=w("index",e,t,a);return[a.getTensorArray(n.id).read(r)]}case"TensorArrayGatherV3":{let n=w("tensorArrayId",e,t,a),r=w("indices",e,t,a),s=w("dtype",e,t,a);return[a.getTensorArray(n.id).gather(r,s)]}case"TensorArrayScatterV3":{let n=w("tensorArrayId",e,t,a),r=w("indices",e,t,a),s=w("tensor",e,t,a),i=a.getTensorArray(n.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=w("tensorArrayId",e,t,a),r=a.getTensorArray(n.id),s=w("dtype",e,t,a);return[r.concat(s)]}case"TensorArraySplitV3":{let n=w("tensorArrayId",e,t,a),r=w("tensor",e,t,a),s=w("lengths",e,t,a),i=a.getTensorArray(n.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let n=w("tensorArrayId",e,t,a),r=a.getTensorArray(n.id);return[ze(r.size(),"int32")]}case"TensorArrayCloseV3":{let n=w("tensorArrayId",e,t,a),r=a.getTensorArray(n.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let n=w("tensorListId",e,t,a),r=w("index",e,t,a),s=w("tensor",e,t,a),i=a.getTensorList(n.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let n=w("tensorListId",e,t,a),r=w("index",e,t,a),s=w("elementShape",e,t,a),i=w("elementDType",e,t,a);return[a.getTensorList(n.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=w("indices",e,t,a),r=w("tensor",e,t,a),s=w("elementShape",e,t,a),i=w("numElements",e,t,a),o=KP(r,n,s,i);return a.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=w("elementShape",e,t,a),r=w("elementDType",e,t,a),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=w(s,e,t,a),o=e.op==="TensorListReserve"?-1:i,l=XP(n,r,i,o);return a.addTensorList(l),[l.idTensor]}case"TensorListGather":{let n=w("tensorListId",e,t,a),r=w("indices",e,t,a),s=w("elementShape",e,t,a),i=w("elementDType",e,t,a);return[a.getTensorList(n.id).gather(r,i,s)]}case"TensorListStack":{let n=w("tensorListId",e,t,a),r=w("elementShape",e,t,a),s=w("elementDType",e,t,a),i=w("numElements",e,t,a);return[a.getTensorList(n.id).stack(r,s,i)]}case"TensorListFromTensor":{let n=w("tensor",e,t,a),r=w("elementShape",e,t,a),s=w("elementDType",e,t,a),i=qP(n,r,s);return a.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=w("tensorListId",e,t,a),r=a.getTensorList(n.id),s=w("dtype",e,t,a),i=w("elementShape",e,t,a);return[r.concat(s,i)]}case"TensorListPushBack":{let n=w("tensorListId",e,t,a),r=w("tensor",e,t,a),s=a.getTensorList(n.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let n=w("tensorListId",e,t,a),r=w("elementShape",e,t,a),s=w("elementDType",e,t,a);return[a.getTensorList(n.id).popBack(r,s)]}case"TensorListSplit":{let n=w("tensor",e,t,a),r=w("elementShape",e,t,a),s=w("lengths",e,t,a),i=ZP(n,s,r);return a.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=w("tensorListId",e,t,a),r=a.getTensorList(n.id);return[ze(r.size(),"int32")]}case"TensorListResize":{let n=w("tensorListId",e,t,a),r=w("size",e,t,a),s=a.getTensorList(n.id).resize(r);return a.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Q5(e,t,a){let[n,r]=w("fusedOps",e,t,a),s=n==="biasadd",i=!s,o=r==="prelu",l=n==="fusedbatchnorm",u=w("numArgs",e,t,a);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let p=w("strides",e,t,a),c=xc(e,t,a),d=w("dataFormat",e,t,a).toUpperCase(),h=w("dilations",e,t,a),[f,m]=w("args",e,t,a);i&&(m=f,f=void 0);let g=w("leakyreluAlpha",e,t,a);return{stride:p,pad:c,dataFormat:d,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var JP=(e,t,a,n=Zt)=>{switch(e.op){case"Conv1D":{let r=w("stride",e,t,a),s=w("pad",e,t,a),i=w("dataFormat",e,t,a).toUpperCase(),o=w("dilation",e,t,a);return[n.conv1d(w("x",e,t,a),w("filter",e,t,a),r,s,i,o)]}case"Conv2D":{let r=w("strides",e,t,a),s=xc(e,t,a),i=w("dataFormat",e,t,a).toUpperCase(),o=w("dilations",e,t,a);return[n.conv2d(w("x",e,t,a),w("filter",e,t,a),[r[1],r[2]],s,i,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:r,pad:s,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:p,leakyreluAlpha:c}=Q5(e,t,a);return[n.fused.conv2d({x:w("x",e,t,a),filter:w("filter",e,t,a),strides:[r[1],r[2]],pad:s,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:p,preluActivationWeights:u,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:s,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:p,leakyreluAlpha:c}=Q5(e,t,a);return[n.fused.depthwiseConv2d({x:w("x",e,t,a),filter:w("filter",e,t,a),strides:[r[1],r[2]],pad:s,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:p,preluActivationWeights:u,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=w("outputShape",e,t,a),s=w("strides",e,t,a),i=xc(e,t,a);return[n.conv2dTranspose(w("x",e,t,a),w("filter",e,t,a),r,[s[1],s[2]],i)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=w("strides",e,t,a),s=xc(e,t,a),i=w("dilations",e,t,a),o=w("dataFormat",e,t,a).toUpperCase();return[n.depthwiseConv2d(w("input",e,t,a),w("filter",e,t,a),[r[1],r[2]],s,o,[i[1],i[2]])]}case"Conv3D":{let r=w("strides",e,t,a),s=w("pad",e,t,a),i=w("dataFormat",e,t,a).toUpperCase(),o=w("dilations",e,t,a);return[n.conv3d(w("x",e,t,a),w("filter",e,t,a),[r[1],r[2],r[3]],s,i,[o[1],o[2],o[3]])]}case"AvgPool":{let r=w("strides",e,t,a),s=w("pad",e,t,a),i=w("kernelSize",e,t,a);return[n.avgPool(w("x",e,t,a),[i[1],i[2]],[r[1],r[2]],s)]}case"MaxPool":{let r=w("strides",e,t,a),s=w("pad",e,t,a),i=w("kernelSize",e,t,a);return[n.maxPool(w("x",e,t,a),[i[1],i[2]],[r[1],r[2]],s)]}case"MaxPoolWithArgmax":{let 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type ${e.op} is not implemented`)}},iF=(e,t,a,n=Zt)=>{switch(e.op){case"Equal":return[n.equal(w("a",e,t,a),w("b",e,t,a))];case"NotEqual":return[n.notEqual(w("a",e,t,a),w("b",e,t,a))];case"Greater":return[n.greater(w("a",e,t,a),w("b",e,t,a))];case"GreaterEqual":return[n.greaterEqual(w("a",e,t,a),w("b",e,t,a))];case"Less":return[n.less(w("a",e,t,a),w("b",e,t,a))];case"LessEqual":return[n.lessEqual(w("a",e,t,a),w("b",e,t,a))];case"LogicalAnd":return[n.logicalAnd(w("a",e,t,a),w("b",e,t,a))];case"LogicalNot":return[n.logicalNot(w("a",e,t,a))];case"LogicalOr":return[n.logicalOr(w("a",e,t,a),w("b",e,t,a))];case"Select":case"SelectV2":return[n.where(w("condition",e,t,a),w("a",e,t,a),w("b",e,t,a))];default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},uF=(e,t,a,n=Zt)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=n.raggedGather(w("paramsNestedSplits",e,t,a),w("paramsDenseValues",e,t,a),w("indices",e,t,a),w("outputRaggedRank",e,t,a));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=n.raggedRange(w("starts",e,t,a),w("limits",e,t,a),w("splits",e,t,a));return[r,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(w("shape",e,t,a),w("values",e,t,a),w("defaultValue",e,t,a),w("rowPartitionTensors",e,t,a),w("rowPartitionTypes",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},dF=(e,t,a,n=Zt)=>{switch(e.op){case"Max":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.max(w("x",e,t,a),o,l)]}case"Mean":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.mean(w("x",e,t,a),o,l)]}case"Min":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.min(w("x",e,t,a),o,l)]}case"Sum":{let 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implemented`)}},pF=(e,t,a,n=Zt)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=w("n",e,t,a),s=w("axis",e,t,a),i=w("tensors",e,t,a);return i=i.slice(0,r),[n.concat(i,s)]}case"Gather":{let r=w("x",e,t,a),s=w("indices",e,t,a);return[n.gather(r,n.cast(s,"int32"),0)]}case"GatherV2":{let r=w("axis",e,t,a),s=w("batchDims",e,t,a),i=w("x",e,t,a),o=w("indices",e,t,a);return[n.gather(i,n.cast(o,"int32"),r,s)]}case"Reverse":{let r=w("dims",e,t,a),s=[];for(let o=0;o<r.length;o++)r[o]&&s.push(o);let i=w("x",e,t,a);return[n.reverse(i,s)]}case"ReverseV2":{let r=w("axis",e,t,a),s=w("x",e,t,a);return[n.reverse(s,r)]}case"Slice":{let r=w("begin",e,t,a),s=w("size",e,t,a);return[n.slice(w("x",e,t,a),r,s)]}case"StridedSlice":{let r=w("begin",e,t,a),s=w("end",e,t,a),i=w("strides",e,t,a),o=w("beginMask",e,t,a),l=w("endMask",e,t,a),u=w("ellipsisMask",e,t,a),p=w("newAxisMask",e,t,a),c=w("shrinkAxisMask",e,t,a),d=w("x",e,t,a);return[n.stridedSlice(d,r,s,i,o,l,u,p,c)]}case"Pack":return Oe(()=>{let r=w("axis",e,t,a),s=w("tensors",e,t,a),i=s[0].shape,o=n.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(n.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:n.reshape(u,i)});return[n.stack(l,r)]});case"Unpack":{let r=w("axis",e,t,a),s=w("tensor",e,t,a);return n.unstack(s,r)}case"Tile":{let r=w("reps",e,t,a);return[n.tile(w("x",e,t,a),r)]}case"Split":case"SplitV":{let r=w("axis",e,t,a),s=w("numOrSizeSplits",e,t,a),i=w("x",e,t,a);return n.split(i,s,r)}case"ScatterNd":{let r=w("indices",e,t,a),s=w("values",e,t,a),i=w("shape",e,t,a);return[n.scatterND(r,s,i)]}case"GatherNd":{let r=w("x",e,t,a),s=w("indices",e,t,a);return[n.gatherND(r,s)]}case"SparseToDense":{let r=w("sparseIndices",e,t,a),s=w("outputShape",e,t,a),i=w("sparseValues",e,t,a),o=w("defaultValue",e,t,a);return[n.sparseToDense(r,i,s,i.dtype===o.dtype?o:n.cast(o,i.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},cF=(e,t,a,n=Zt)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=n.sparse.sparseFillEmptyRows(w("indices",e,t,a),w("values",e,t,a),w("denseShape",e,t,a),w("defaultValue",e,t,a));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=n.sparse.sparseReshape(w("inputIndices",e,t,a),w("inputShape",e,t,a),w("newShape",e,t,a));return[r,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(w("data",e,t,a),w("indices",e,t,a),w("segmentIds",e,t,a))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(w("data",e,t,a),w("indices",e,t,a),w("segmentIds",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hF=(e,t,a,n=Zt)=>{switch(e.op){case"FFT":return[n.fft(w("x",e,t,a))];case"IFFT":return[n.ifft(w("x",e,t,a))];case"RFFT":return[n.rfft(w("x",e,t,a))];case"IRFFT":return[n.irfft(w("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},fF=(e,t,a,n=Zt)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=n.string.stringNGrams(w("data",e,t,a),w("dataSplits",e,t,a),w("separator",e,t,a),w("nGramWidths",e,t,a),w("leftPad",e,t,a),w("rightPad",e,t,a),w("padWidth",e,t,a),w("preserveShortSequences",e,t,a));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=n.string.stringSplit(w("input",e,t,a),w("delimiter",e,t,a),w("skipEmpty",e,t,a));return[r,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(w("input",e,t,a),w("numBuckets",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mF=(e,t,a,n=Zt)=>{switch(e.op){case"Cast":return[n.cast(w("x",e,t,a),w("dtype",e,t,a))];case"ExpandDims":{let r=w("axis",e,t,a);return[n.expandDims(w("x",e,t,a),r)]}case"Squeeze":{let r=w("axis",e,t,a);return[n.squeeze(w("x",e,t,a),r)]}case"Reshape":return[n.reshape(w("x",e,t,a),w("shape",e,t,a))];case"MirrorPad":return[n.mirrorPad(w("x",e,t,a),w("padding",e,t,a),w("mode",e,t,a))];case"PadV2":case"Pad":return[n.pad(w("x",e,t,a),w("padding",e,t,a),w("constantValue",e,t,a))];case"SpaceToBatchND":{let r=w("blockShape",e,t,a),s=w("paddings",e,t,a);return[n.spaceToBatchND(w("x",e,t,a),r,s)]}case"BatchToSpaceND":{let r=w("blockShape",e,t,a),s=w("crops",e,t,a);return[n.batchToSpaceND(w("x",e,t,a),r,s)]}case"DepthToSpace":{let r=w("blockSize",e,t,a),s=w("dataFormat",e,t,a).toUpperCase();return[n.depthToSpace(w("x",e,t,a),r,s)]}case"BroadcastTo":return[n.broadcastTo(w("x",e,t,a),w("shape",e,t,a))];case"BroadcastArgs":return[n.broadcastArgs(w("s0",e,t,a),w("s1",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ey(e,t,a,n,r=Oe){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>GP(i,o,l));case"basic_math":return r(()=>HP(i,o,l));case"control":return YP(i,o,l);case"convolution":return r(()=>JP(i,o,l));case"creation":return r(()=>QP(i,o,l));case"dynamic":return eF(i,o,l);case"evaluation":return r(()=>tF(i,o,l));case"image":return r(()=>sF(i,o,l));case"graph":return r(()=>aF(i,o,l));case"logical":return r(()=>iF(i,o,l));case"matrices":return r(()=>oF(i,o,l));case"normalization":return r(()=>lF(i,o,l));case"ragged":return r(()=>uF(i,o,l));case"reduction":return r(()=>dF(i,o,l));case"slice_join":return r(()=>pF(i,o,l));case"sparse":return r(()=>cF(i,o,l));case"spectral":return r(()=>hF(i,o,l));case"string":return r(()=>fF(i,o,l));case"transformation":return r(()=>mF(i,o,l));case"hash_table":return rF(i,o,l,n);case"custom":let u=v4(i.op);if(u&&u.customExecutor)return u.customExecutor(new UP(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.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,a);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var ty=class{constructor(e={},t={},a={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=a,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 a=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(a))}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 ay(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>qa(d)[0]),p=[];n!=null&&(p=n.map(d=>qa(d.name)[0]));let c=[...t];for(;c.length>0;){let d=c.pop();if((U4(d)||bF(d)||vF(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&u.indexOf(d.name)===-1&&p.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function gF(e,t,a){let{usedNodes:n,inputs:r}=a,s=[],i=Object.keys(r).map(p=>qa(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{n.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{n.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{n.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(c=>{!l.has(c.name)&&n.has(c.name)&&c.inputs.every(d=>l.has(d.name))&&s.push(c)})}return u}var yF=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],xF=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],AF=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function U4(e){return yF.indexOf(e.op)>=0}function bF(e){return xF.indexOf(e.op)>=0}function vF(e){return AF.indexOf(e.op)>=0}var f2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(a=>{this._functionExecutorMap[a]=new f2(e.functions[a],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(a=>e[a].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 a=e.map(r=>r.name).sort(),n=t.map(r=>r.name).sort();return a.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let a=ay(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:r,syncInputs:s}=a;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 [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return gF(this.graph,this.weightMap,a)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return On(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,a])=>[t,this.cloneTensorList(a)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let a=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=a.map(p=>this.graph.nodes[qa(p)[0]]),r=t.map(p=>qa(p)[0]),s=r.map(p=>this.graph.nodes[p]);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));try{this.keepIntermediateTensors=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},u={};return Oe(()=>{let p=new ty(this.weightMap,l,u,this.functionExecutorMap),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(f=>{let[m,g]=qa(f),y=[];y[g]=e[f],c[m]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(y))});let d=this.getFrozenTensorIds(c),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!c[m.name]){let g=ey(m,c,p,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);c[m.name]=g,this.keepIntermediateTensors&&(this.clonedTensorsMap[m.name]=this.cloneTensorList(g)),this.checkTensorForDisposal(m.name,m,c,p,d,r,h)}}return this.parent==null&&p.dispose(d),t.map(f=>va(f,c,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(a=>e[a]).map(a=>a.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,a,n,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(a[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=kP(o.name,a,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,a=!1,n={},r={}){this.disposeIntermediateTensors(),a||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let s=new ty(this.weightMap,n,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,a),o=t.map(c=>va(c,i,s)),l=o.map(c=>c.id),u=Object.keys(e).map(c=>e[c].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(c=>{c.forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,a){let n=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(n,this.outputNodes,!0,t,a)}async executeWithControlFlow(e,t,a,n){let r=Object.keys(e),s=r.map(x=>this.graph.nodes[qa(x)[0]]),i=a.map(x=>qa(x)[0]),o=i.map(x=>this.graph.nodes[x]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:c}=ay(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(x=>{let[A,b]=qa(x),k=[];k[b]=e[x],h[A]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let x=this.processStack(s,d,t,h,g,m,i,f,l);await Promise.all(x)}p==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(x=>!U4(x)&&!va(x.name,h,t)).map(x=>x.name);if(y.length>0){let x="";throw p!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${x}`)}return h}processStack(e,t,a,n,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();a.currentContext=p.contexts;let c="";if(p.node.op==="Enter"&&w("isConstant",p.node,n,a)&&([c]=gr(p.node.name,a)),n[p.node.name]==null){let d=ey(p.node,n,a,this._resourceManager);c||([c]=gr(p.node.name,a));let h=a.currentContext;v.isPromise(d)?u.push(d.then(f=>(n[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=h,this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l),f))):(n[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l))}else this.processChildNodes(p.node,t,a,n,r,l)}return u}processChildNodes(e,t,a,n,r,s){e.children.forEach(i=>{let[o]=gr(i.name,a);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!va(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})):i.inputNames.every(l=>!!va(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let a=e[t],[n]=qa(t),r=this.graph.nodes[n];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===a.shape.length&&a.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${a.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(a.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(e){var t,a;let n={};for(let r in e){let s=(a=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||a===void 0?void 0:a[r];s!=null?n[s.name]=e[r]:n[r]=e[r]}return n}checkInputs(e){let t=Object.keys(e).filter(a=>{let[n]=qa(a);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var a,n;let r=(n=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||n===void 0?void 0:n[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[a]=qa(t);if(!this.graph.nodes[a])throw new Error(`The output '${t}' is not found in the graph`)})}},kF=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new f2(Z5.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Z5.Instance.transformGraph(e.modelInitializer);this.initializer=new f2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return 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Z4=Lt((e,t)=>e===t?1:0),Y4=Yt(fi,Z4,null,"bool"),LF={kernelName:fi,backendName:"cpu",kernelFunc:Y4},J4=ls(e=>Math.exp(e)),Q4=ou(mi,J4,"float32"),BF={kernelName:mi,backendName:"cpu",kernelFunc:Q4},e7=ls(e=>Math.expm1(e)),WF=ou(_l,e7),VF={kernelName:_l,backendName:"cpu",kernelFunc:WF},t7=ls(e=>Math.floor(e)),UF=ou(yi,t7),GF={kernelName:yi,backendName:"cpu",kernelFunc:UF};function a7(e,t,a,n,r,s,i,o,l){let u=_e([n,s],a);for(let p=0;p<n;p++){let c=[],d=0;for(let h=0;h<r;h++){let f=e[p*r+h];d+=f*i[h],c.push(f)}if(d<0||d>=l/s)throw new Error(`Invalid indices: ${c} does not index into ${o}`);for(let h=0;h<s;h++)u.values[p*s+h]=t.get(...t.indexToLoc(d*s+h))}return u}function n7(e,t,a){let n=_e(a,e.dtype);for(let r=0;r<n.size;++r){let s=n.indexToLoc(r).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let u=e.locToIndex(s);0<=u&&u<e.values.length&&(n.values[r]=e.values[u])}return n}var 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h=a.data.get(c.dataId).values,{outVals:f,outShape:m,outDtype:g}=m7(c.shape,c.dtype,h,p),y=m;return i&&(y=T.expandShapeToKeepDim(m,l)),d.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.makeTensorInfo(y,g,f)}var hO={kernelName:qi,backendName:"cpu",kernelFunc:cO};function fO(e,t,a){e.forEach((n,r)=>{if(n<0||n>=a){let s=v.indexToLoc(r,t.length,v.computeStrides(t)).join(",");throw new Error(`indices[${s}] = ${n} is not in [0, ${a})`)}})}function mO(e,t){for(let a=0;a<e.length;++a){let n=e[a],r=a===e.length-1?t:e[a+1].length;if(n.length===0)throw new Error("Ragged splits may not be empty");if(n[0]<0)throw new Error("Ragged splits must be non-negative");if(n[n.length-1]>r)throw new Error("Ragged splits must not point past values");for(let s=1;s<n.length;++s)if(n[s-1]>n[s])throw new Error("Ragged splits must be sorted in ascending order")}}function gO(e,t,a,n){let r=[],s=0,i=t.length-1+a.length,o=new Array(i).fill(null).map(()=>[0]);mO(a,n);let l=1;for(let u=0;u<t.length-1;++u){l*=t[u];let p=t[u+1];for(let c=1;c<l+1;++c)o[u].push(c*p)}for(let u=0;u<e.length;++u){let p=e[u],c=e[u]+1;for(let d=0;d<a.length;++d){let h=a[d],f=d+t.length-1;if(f>=0){let m=o[f],g=m[m.length-1]-h[p];for(let y=p;y<c;++y)o[f].push(h[y+1]+g)}p=h[p],c=h[c]}c!==p&&(r.push([p,c]),s+=c-p)}return{outSplits:o,valueSlices:r,numValues:s}}function yO(e){let t=[];for(let a=0;a<e.length;++a){let n=e[a].length,r=v.getArrayFromDType("int32",n);t.push(r),e[a].forEach((s,i)=>r[i]=s)}return t}function ry(e,t){let a=e.slice(0,t);for(;a.length<t;)a.push(1);for(let n=t;n<e.length;n++)a[t-1]*=e[n];return a}function xO(e,t,a,n,r,s){let i=ry(t,2)[1],o=ry(s,2)[1],l=0;for(let u of a)for(let p=u[0];p<u[1];++p){for(let c=0;c<n;++c)r[l*o+c]=e[p*i+c];++l}}function AO(e,t,a,n,r){let s=t.slice();s[0]=r;let i=v.getArrayFromDType(a,v.sizeFromShape(s)),o=e.length,l=o===0?0:o/t[0];return xO(e,t,n,l,i,s),[i,s]}function g7(e,t,a,n,r,s,i,o){if(e.length===0)throw new Error("paramsNestedSplits must be non 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if(b=Math.ceil(Math.abs((x-y)/A)),b>sy)throw new Error(`Requires ((limit - start) / delta) <= ${sy}`);d[g+1]=d[g]+b}let h=d[c],f=v.getArrayFromDType(a,h),m=0;for(let g=0;g<c;++g){let y=d[g+1]-d[g],x=o?e[0]:e[g],A=u?s[0]:s[g];for(let b=0;b<y;++b)f[m++]=x,x+=A}return[d,f]}var kn=T.RowPartitionType,m2=class{constructor(e,t,a,n,r,s,i,o,l,u){this.shape=e,this.shapeShape=t,this.values=a,this.valuesShape=n,this.valuesDType=r,this.defaultValue=s,this.defaultValueShape=i,this.rowPartitionValues=o,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=T.getRowPartitionTypesHelper(u),this.raggedRank=T.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===kn.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===kn.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){let t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case kn.VALUE_ROWIDS:return m2.getMaxWidthValueRowID(t);case kn.ROW_SPLITS:return m2.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${kn[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){let t=e.length;if(t===0||t===1)return 0;let a=0;for(let n=0;n<t-1;++n){let r=e[n+1]-e[n];r>a&&(a=r)}return a}static getMaxWidthValueRowID(e){let t=e.length;if(t===0)return 0;let a=0,n=e[0],r=0;for(let s=1;s<t;++s){let i=e[s];i!==n&&(n=i,r=Math.max(s-a,r),a=s)}return Math.max(t-a,r)}tensorShapeFromTensor(e,t,a=!0){if(t.length===0){if(e[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return oy(e,a)}calculateOutputSize(e){let t=this.valuesShape,a=this.defaultValueShape;T.validateDefaultValueShape(a,t);let n=this.tensorShapeFromTensor(this.shape,this.shapeShape),r=T.combineRaggedTensorToTensorShapes(this.raggedRank,n,t);r[0]<0&&(r[0]=e);for(let s=1;s<=this.raggedRank;++s)r[s]<0&&(r[s]=this.getMaxWidth(s));return r}calculateFirstParentOutputIndex(e,t,a){let n=Math.min(e,a),r=[],s=0;for(let i=0;i<n;++i,s+=t)r.push(s);for(let i=n;i<e;++i)r.push(-1);return v.assert(r.length===e,()=>"Final length of result must be equal to firstDimension."),r}calculateOutputIndexRowSplit(e,t,a,n){let r=e.length,s=[];for(let i=0;i<r-1;++i){let o=e[i+1]-e[i],l=Math.min(n,o),u=t[i];u===-1&&(l=0);for(let p=0;p<l;++p)s.push(u),u+=a;for(let p=0;p<o-l;++p)s.push(-1)}if(r>0&&s.length!==e[r-1])throw new Error("Invalid row split size.");return s}calculateOutputIndexValueRowID(e,t,a,n){let r=e.length,s=[];if(r===0)return[];let i=0,o=e[0];if(o>=t.length)throw new Error(`Got currentValueRowId=${o}, which is not less than ${t.length}`);let l=t[o];s.push(l);for(let u=1;u<r;++u){let p=e[u];if(p===o)l>=0&&(++i,i<n?l+=a:l=-1);else{if(i=0,o=p,p>=t.length)throw new Error(`Got nextValueRowId=${p} which is not less than ${t.length}`);l=t[p]}s.push(l)}if(s.length!==e.length)throw new Error("Invalid row ids.");return s}calculateOutputIndex(e,t,a,n){let r=this.getRowPartitionTensor(e),s=this.getRowPartitionTypeByDimension(e);switch(s){case kn.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(r,t,a,n);case kn.ROW_SPLITS:if(r.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${r.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(r,t,a,n);default:throw new Error(`Unsupported partition type: ${kn[s]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let t=this.rowPartitionTypes[0];switch(t){case kn.FIRST_DIM_SIZE:return e[0];case kn.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case kn.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${kn[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");let e=this.getFirstDimensionSize(),t=this.calculateOutputSize(e),a=new Array(this.raggedRank+1);a[a.length-1]=1;for(let s=a.length-2;s>=0;--s)a[s]=a[s+1]*t[s+1];let n=oy(t,!1),r=v.getArrayFromDType(this.valuesDType,v.sizeFromShape(n));if(a[0]*t[0]>0){let s=this.calculateFirstParentOutputIndex(e,a[0],t[0]);for(let i=1;i<=this.raggedRank;++i)s=this.calculateOutputIndex(i-1,s,a[i],t[i]);this.setOutput(this.raggedRank,s,r,n)}return[n,r]}setOutput(e,t,a,n){if(a.length===0)return;let r=this.values,s=a,i=n.slice();i=i.slice(e+1);let o=v.sizeFromShape(i),l=t.length,u=this.defaultValue;if(u.length!==o&&u.length!==1){let h=this.defaultValueShape;Fe(()=>{let f=Q(u,h);u=rl(f,i).dataSync()})}let p=0,c=0,d=0;for(let h=0;h<=l;++h){let f=h<l?t[h]:-1;if(f===d){++d;continue}if(c<d){let m=r.subarray(p*o),g=s.subarray(c*o),y=(d-c)*o;iy(g,m,y)}if(h>=l){let 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wD(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Ae([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=p.strideHeight,d=p.strideWidth,h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,x=p.effectiveFilterWidth,A=x-1-p.padInfo.left,b=y-1-p.padInfo.top,k=_e(i.shape,"float32"),S=1/(h*f),C=a.data.get(r.dataId).values,N=_e(r.shape,"float32",C);for(let $=0;$<p.batchSize;++$)for(let M=0;M<p.inChannels;++M)for(let R=0;R<p.inHeight;++R)for(let I=0;I<p.inWidth;++I){let _=R-b,D=I-A,W=0;for(let P=0;P<y;P+=m){let U=(_+P)/c;if(!(U<0||U>=p.outHeight||Math.floor(U)!==U))for(let G=0;G<x;G+=g){let q=(D+G)/d;if(q<0||q>=p.outWidth||Math.floor(q)!==q)continue;let H=N.get($,U,q,M);W+=H}}k.set(W*S,$,R,I,M)}return a.makeTensorInfo(k.shape,k.dtype,k.values)}var ID={kernelName:Kc,backendName:"cpu",kernelFunc:wD};function 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a.makeTensorInfo(r.shape,r.dtype,m)}var TD={kernelName:Ai,backendName:"cpu",kernelFunc:SD};function CD(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;Ae([r],"batchToSpaceND");let o=s.reduce((y,x)=>y*x),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=mt({inputs:{x:r},backend:a,attrs:{shape:l}}),f=Ba({inputs:{x:h},backend:a,attrs:{perm:u}}),m=mt({inputs:{x:f},backend:a,attrs:{shape:p}}),g=Gs({inputs:{x:m},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),g}var ND={kernelName:El,backendName:"cpu",kernelFunc:CD};function ED(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=o3(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var RD={kernelName:Td,backendName:"cpu",kernelFunc:ED};function MD(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var $D={kernelName:Yc,backendName:"cpu",kernelFunc:MD},_D=lt(ns,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e<a.clipValueMin?a.clipValueMin:e}),PD={kernelName:ns,backendName:"cpu",kernelFunc:_D},FD=e=>{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let p=o[u],c=l[u];n[u]=Math.hypot(p,c)}return a.makeOutput(n,t.shape,"float32")},OD={kernelName:Jc,backendName:"cpu",kernelFunc:FD};function ml(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.data.get(n.dataId).complexTensorInfos.imag,s=a.data.get(r.dataId).values;return a.makeTensorInfo(r.shape,r.dtype,s)}var DD={kernelName:Fd,backendName:"cpu",kernelFunc:ml};function gl(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(m=>m.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(m=>v.sizeFromShape(m.shape)>0);if(l.length===1)return er({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let m=l.map(b=>Us({inputs:{input:b},backend:a})),g=l.map(b=>ml({inputs:{input:b},backend:a})),y=gl({inputs:m,backend:a,attrs:{axis:s}}),x=gl({inputs:g,backend:a,attrs:{axis:s}}),A=Ka({inputs:{real:y,imag:x},backend:a});return 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BD(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n;Ae([r,s],"conv2dBackpropFilter");let c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,y=d.dataFormat==="channelsLast",x=new jt(d.filterShape,"float32"),A=d.padInfo.left,b=d.padInfo.top,k=a.data.get(r.dataId).values,S=a.data.get(s.dataId).values,C=new jt(r.shape,r.dtype,k),N=new jt(s.shape,s.dtype,S);for(let $=0;$<m;++$){let M=Math.max(0,Math.ceil((b-$)/h)),R=Math.min(d.outHeight,(d.inHeight+b-$)/h);for(let I=0;I<g;++I){let _=Math.max(0,Math.ceil((A-I)/f)),D=Math.min(d.outWidth,(d.inWidth+A-I)/f);for(let W=0;W<d.inChannels;++W)for(let P=0;P<d.outChannels;++P){let U=0;for(let G=0;G<d.batchSize;++G)for(let q=M;q<R;++q){let H=$+q*h-b;for(let B=_;B<D;++B){let Z=I+B*f-A;y?U+=C.get(G,H,Z,W)*N.get(G,q,B,P):U+=C.get(G,W,H,Z)*N.get(G,P,q,B)}}x.set(U,$,I,W,P)}}}return a.makeTensorInfo(x.shape,x.dtype,x.values)}var WD={kernelName:Nd,backendName:"cpu",kernelFunc:BD};function VD(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n;Ae([r,s],"conv2dBackpropInput");let c=v.computeStrides(s.shape),d=v.computeStrides(r.shape),h=T.convertConv2DDataFormat(u),f=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,h),m=new jt(f.inShape,"float32"),g=m.values,y=a.data.get(r.dataId).values,x=a.data.get(s.dataId).values,[A,b,k]=c,{batchSize:S,filterHeight:C,filterWidth:N,inChannels:$,inHeight:M,inWidth:R,outChannels:I,outHeight:_,outWidth:D,strideHeight:W,strideWidth:P}=f;h=f.dataFormat;let U=C-1-f.padInfo.top,G=N-1-f.padInfo.left,q=h==="channelsLast",H=m.strides[0],B=q?m.strides[1]:m.strides[2],Z=q?m.strides[2]:1,X=q?1:m.strides[1],ne=d[0],ee=q?d[1]:d[2],ce=q?d[2]:1,ie=q?1:d[1];for(let me=0;me<S;++me)for(let Se=0;Se<$;++Se)for(let Ne=0;Ne<M;++Ne){let 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R=M*N[0],I=M*b.strides[0];for(let _=0;_<u.outDepth;++_){let D=I+_*b.strides[1],W=_*u.strideDepth-y;for(let P=0;P<p;++P){let U=W+P*h;if(U<0||U>=u.inDepth)continue;let G=P*$[0],q=R+U*N[1];for(let H=0;H<u.outHeight;++H){let B=D+H*b.strides[2],Z=H*u.strideHeight-A;for(let X=0;X<c;++X){let ne=Z+X*f;if(ne<0||ne>=u.inHeight)continue;let ee=G+X*$[1],ce=q+ne*N[2];for(let ie=0;ie<u.outWidth;++ie){let me=B+ie*u.outChannels,Se=ie*u.strideWidth-x;for(let Ne=0;Ne<d;++Ne){let Be=Se+Ne*m;if(Be<0||Be>=u.inWidth)continue;let qe=ee+Ne*$[2],dt=ce+Be*u.inChannels,it=qe;for(let at=0;at<u.inChannels;++at){let nt=k[dt+at];for(let Ge=0;Ge<u.outChannels;++Ge)C[me+Ge]+=nt*S[it+Ge];it+=u.outChannels}}}}}}}}return a.makeTensorInfo(b.shape,b.dtype,b.values)}var HD={kernelName:Qc,backendName:"cpu",kernelFunc:GD};function jD(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n;Ae([r,s],"conv3dBackpropFilterV2");let u=v.computeStrides(r.shape),p=v.computeStrides(s.shape),c=T.computeConv3DInfo(r.shape,l,i,1,o),d=c.strideDepth,h=c.strideHeight,f=c.strideWidth,m=c.filterDepth,g=c.filterHeight,y=c.filterWidth,x=new jt(c.filterShape,"float32"),A=x.values,[b,k,S,C]=x.strides,N=a.data.get(s.dataId).values,[$,M,R,I]=p,_=a.data.get(r.dataId).values,[D,W,P,U]=u,G=c.padInfo.front,q=c.padInfo.left,H=c.padInfo.top;for(let B=0;B<m;++B){let Z=Math.max(0,Math.ceil((G-B)/d)),X=Math.min(c.outDepth,(c.inDepth+G-B)/d),ne=B*b;for(let ee=0;ee<g;++ee){let ce=Math.max(0,Math.ceil((H-ee)/h)),ie=Math.min(c.outHeight,(c.inHeight+H-ee)/h),me=ee*k+ne;for(let Se=0;Se<y;++Se){let Ne=Math.max(0,Math.ceil((q-Se)/f)),Be=Math.min(c.outWidth,(c.inWidth+q-Se)/f),qe=Se*S+me;for(let dt=0;dt<c.inChannels;++dt){let it=dt*C+qe;for(let at=0;at<c.outChannels;++at){let nt=0;for(let Ge=0;Ge<c.batchSize;++Ge){let ht=Ge*D,Ua=Ge*$;for(let Ot=Z;Ot<X;++Ot){let on=(B+Ot*d-G)*W+ht,ra=Ot*M+Ua;for(let _a=ce;_a<ie;++_a){let ln=(ee+_a*h-H)*P+on,Pa=_a*R+ra;for(let ut=Ne;ut<Be;++ut){let Fa=(Se+ut*f-q)*U+ln,Ga=ut*I+Pa;nt+=_[Fa+dt]*N[Ga+at]}}}}A[it+at]=nt}}}}}return a.makeTensorInfo(x.shape,x.dtype,x.values)}var qD={kernelName:B2,backendName:"cpu",kernelFunc:jD};function XD(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n;Ae([r],"conv3dBackpropInputV2");let u=v.computeStrides(r.shape),p=v.computeStrides(s.shape),c=T.computeConv3DInfo(l,s.shape,o,1,i),d=new jt(c.inShape,"float32"),h=d.values,[f,m,g,y]=d.strides,x=a.data.get(r.dataId).values,[A,b,k,S]=u,C=a.data.get(s.dataId).values,[N,$,M,R]=p,{batchSize:I,filterDepth:_,filterHeight:D,filterWidth:W,inChannels:P,inDepth:U,inHeight:G,inWidth:q,outChannels:H,outDepth:B,outHeight:Z,outWidth:X,strideDepth:ne,strideHeight:ee,strideWidth:ce}=c,ie=_-1-c.padInfo.front,me=D-1-c.padInfo.top,Se=W-1-c.padInfo.left;for(let Ne=0;Ne<I;++Ne)for(let Be=0;Be<P;++Be)for(let qe=0;qe<U;++qe){let 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c=fa(u.dtype,"int32"),d=v.makeZerosTypedArray(v.sizeFromShape(u.shape),c),h=a.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,x)=>y+f-x-1:(y,x)=>y+x;for(let y=0;y<h.length;y+=f)for(let x=0;x<f;x++){let A=m(y,x);if(x===0)d[A]=i?0:h[A];else{let b=m(y,x-1);d[A]=i?h[b]+d[b]:h[A]+d[b]}}let g=a.makeTensorInfo(u.shape,c,d);if(l!=null){let y=T.getUndoAxesPermutation(l),x=Ba({inputs:{x:g},backend:a,attrs:{perm:y}});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(u),x}return g}var sz={kernelName:li,backendName:"cpu",kernelFunc:rz};function iz(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.data.get(r.dataId).values,u=a.data.get(s.dataId).values,p=o3(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=X4(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be 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if(b=Math.ceil(Math.abs((x-y)/A)),b>ry)throw new Error(`Requires ((limit - start) / delta) <= ${ry}`);d[g+1]=d[g]+b}let h=d[c],f=v.getArrayFromDType(a,h),m=0;for(let g=0;g<c;++g){let y=d[g+1]-d[g],x=o?e[0]:e[g],A=u?s[0]:s[g];for(let b=0;b<y;++b)f[m++]=x,x+=A}return[d,f]}var kn=T.RowPartitionType,m2=class{constructor(e,t,a,n,r,s,i,o,l,u){this.shape=e,this.shapeShape=t,this.values=a,this.valuesShape=n,this.valuesDType=r,this.defaultValue=s,this.defaultValueShape=i,this.rowPartitionValues=o,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=T.getRowPartitionTypesHelper(u),this.raggedRank=T.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===kn.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===kn.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){let 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n=this.tensorShapeFromTensor(this.shape,this.shapeShape),r=T.combineRaggedTensorToTensorShapes(this.raggedRank,n,t);r[0]<0&&(r[0]=e);for(let s=1;s<=this.raggedRank;++s)r[s]<0&&(r[s]=this.getMaxWidth(s));return r}calculateFirstParentOutputIndex(e,t,a){let n=Math.min(e,a),r=[],s=0;for(let i=0;i<n;++i,s+=t)r.push(s);for(let i=n;i<e;++i)r.push(-1);return v.assert(r.length===e,()=>"Final length of result must be equal to firstDimension."),r}calculateOutputIndexRowSplit(e,t,a,n){let r=e.length,s=[];for(let i=0;i<r-1;++i){let o=e[i+1]-e[i],l=Math.min(n,o),u=t[i];u===-1&&(l=0);for(let p=0;p<l;++p)s.push(u),u+=a;for(let p=0;p<o-l;++p)s.push(-1)}if(r>0&&s.length!==e[r-1])throw new Error("Invalid row split size.");return s}calculateOutputIndexValueRowID(e,t,a,n){let r=e.length,s=[];if(r===0)return[];let i=0,o=e[0];if(o>=t.length)throw new Error(`Got currentValueRowId=${o}, which is not less than ${t.length}`);let l=t[o];s.push(l);for(let u=1;u<r;++u){let p=e[u];if(p===o)l>=0&&(++i,i<n?l+=a:l=-1);else{if(i=0,o=p,p>=t.length)throw new Error(`Got nextValueRowId=${p} which is not less than ${t.length}`);l=t[p]}s.push(l)}if(s.length!==e.length)throw new Error("Invalid row ids.");return s}calculateOutputIndex(e,t,a,n){let r=this.getRowPartitionTensor(e),s=this.getRowPartitionTypeByDimension(e);switch(s){case kn.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(r,t,a,n);case kn.ROW_SPLITS:if(r.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${r.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(r,t,a,n);default:throw new Error(`Unsupported partition type: ${kn[s]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let t=this.rowPartitionTypes[0];switch(t){case kn.FIRST_DIM_SIZE:return e[0];case kn.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case kn.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${kn[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");let e=this.getFirstDimensionSize(),t=this.calculateOutputSize(e),a=new Array(this.raggedRank+1);a[a.length-1]=1;for(let s=a.length-2;s>=0;--s)a[s]=a[s+1]*t[s+1];let n=iy(t,!1),r=v.getArrayFromDType(this.valuesDType,v.sizeFromShape(n));if(a[0]*t[0]>0){let s=this.calculateFirstParentOutputIndex(e,a[0],t[0]);for(let i=1;i<=this.raggedRank;++i)s=this.calculateOutputIndex(i-1,s,a[i],t[i]);this.setOutput(this.raggedRank,s,r,n)}return[n,r]}setOutput(e,t,a,n){if(a.length===0)return;let r=this.values,s=a,i=n.slice();i=i.slice(e+1);let o=v.sizeFromShape(i),l=t.length,u=this.defaultValue;if(u.length!==o&&u.length!==1){let h=this.defaultValueShape;Oe(()=>{let f=Q(u,h);u=rl(f,i).dataSync()})}let p=0,c=0,d=0;for(let h=0;h<=l;++h){let f=h<l?t[h]:-1;if(f===d){++d;continue}if(c<d){let m=r.subarray(p*o),g=s.subarray(c*o),y=(d-c)*o;sy(g,m,y)}if(h>=l){let m=a.length;f=Math.floor(m/o)}if(f>d)if(this.defaultValue.length===1)s.subarray(d*o,f*o).fill(this.defaultValue[0]),d=f;else for(;f>d;){let m=s.slice(d*o);sy(m,u,o),++d}f<0?(p=h+1,c=d):(p=h,c=d,d=c+1)}}};function sy(e,t,a){for(let n=0;n<a;n++)e[n]=t[n]}function iy(e,t){let a=[];for(let n of e){if(n<0){if(!t)throw new Error(`Dimension ${n} must be >= 0`);if(n<-1)throw new Error(`Dimension ${n} must be >= -1`);n=-1}a.push(n)}return a}function y7(e,t,a,n,r,s,i,o,l,u){return new m2(e,t,a,n,r,s,i,o,l,u).compute()}function p3(e,t,a,n){let r=e===t,s=e<t&&a<0,i=t<e&&a>1;if(r||s||i)return v.makeZerosTypedArray(0,n);let o=Math.abs(Math.ceil((t-e)/a)),l=v.makeZerosTypedArray(o,n);t<e&&a===1&&(a=-1),l[0]=e;for(let u=1;u<l.length;u++)l[u]=l[u-1]+a;return l}var x7=ls(e=>1/Math.sqrt(e)),bO=ou(to,x7),vO={kernelName:to,backendName:"cpu",kernelFunc:bO};function el(e,t,a,n,r,s,i,o,l,u){let p=[n/r,r],c=e.values,d=t.values;if(n===0)return _e(a,t.dtype);let h=_e(p,t.dtype);typeof l=="string"||typeof l=="number"?h.values.fill(l):typeof l=="boolean"&&h.values.fill(+l);for(let f=0;f<s;f++){let m=[],g=0;for(let y=0;y<i;y++){let x=c[f*i+y];m.push(x),g+=x*o[y]}if(g<0||g>=n/r)throw new Error(`Invalid indices: ${m} does not index into ${a}`);for(let y=0;y<r;y++)u?h.values[g*r+y]+=d[f*r+y]:h.values[g*r+y]=t.rank===0?d[0]:d[f*r+y]}return h}var kO=ls(e=>1/(1+Math.exp(-e))),A7=lt(ro,e=>1/(1+Math.exp(-e))),wO={kernelName:ro,backendName:"cpu",kernelFunc:A7};function Fc(e,t,a,n,r){let s=St.isSliceContinous(n,t,a),i=v.sizeFromShape(a),o=v.computeStrides(n);if(s){let c=St.computeFlatOffset(t,o);return r==="string"?e.slice(c,c+i):e.subarray(c,c+i)}let l=r==="string"?T.fromUint8ToStringArray(e):e,u=_e(n,r,l),p=_e(a,r);for(let c=0;c<p.size;++c){let d=p.indexToLoc(c),h=d.map((f,m)=>f+t[m]);p.set(u.get(...h),...d)}return r==="string"?T.fromStringArrayToUint8(p.values):p.values}function Gs(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n;Ae(r,"slice");let[o,l]=St.parseSliceParams(r,s,i);St.assertParamsValid(r,o,l);let u=a.data.get(r.dataId).values,p=Fc(u,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,p)}var IO={kernelName:Xl,backendName:"cpu",kernelFunc:Gs};function b7(e,t,a,n,r,s,i){let o=t[0],l=s[0],u=new Array(l),p=new Array(o),c=t[1];if(l===0){if(o!==0)throw new Error(T.getSparseFillEmptyRowsIndicesDenseShapeMismatch(o));let g=v.getArrayFromDType(a,0),y=v.getArrayFromDType(r,0);return[g,[0,c],y,u,p]}let d=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<o;++g){let y=e[g*c];if(y<0)throw new Error(T.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,y));if(y>=l)throw new Error(T.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,y,l));++f[y],d=d&&y>=h,h=y}let m=!0;for(let g=0;g<l;++g){let y=f[g]===0;u[g]=y,m=m&&!y,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&d){let g=e,y=n;for(let x=0;x<o;++x)p[x]=x;return[g,[o,c],y,u,p]}else{let g=f[l-1],y=v.getArrayFromDType(a,g*c),x=v.getArrayFromDType(r,g),A=new Array(l).fill(0);for(let b=0;b<o;++b){let k=e[b*c],S=A[k],C=(k===0?0:f[k-1])+S;A[k]++;for(let N=0;N<c;++N)y[C*c+N]=e[b*c+N];x[C]=n[b],p[b]=C}for(let b=0;b<l;++b)if(A[b]===0){let k=b===0?0:f[b-1];y[k*c+0]=b;for(let S=1;S<c;++S)y[k*c+S]=0;x[k]=i}return[y,[g,c],x,u,p]}}function v7(e,t,a,n,r){let s=v.sizeFromShape(n),i=t[0],o=r.length,l=[],u=1,p=-1;for(let m=0;m<o;++m){let g=r[m];if(g===-1){if(p!==-1)throw new Error(T.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(p,m));p=m,l.push(1)}else{if(g<0)throw new Error(T.getSparseReshapeNegativeOutputDimErrorMessage(m,g));u*=g,l.push(g)}}if(p!==-1){if(u<=0)throw new Error(T.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let m=Math.trunc(s/u);if(u*m!==s)throw new Error(T.getSparseReshapeInputOutputMultipleErrorMessage(n,l));l[p]=m}if(v.sizeFromShape(l)!==s)throw new Error(T.getSparseReshapeInputOutputMismatchErrorMessage(n,l));let c=n.length,d=[];if(c>0){d[c-1]=1;for(let m=c-2;m>=0;--m)d[m]=d[m+1]*n[m+1]}let h=[];if(o>0){h[o-1]=1;for(let m=o-2;m>=0;--m)h[m]=h[m+1]*l[m+1]}let f=v.getArrayFromDType(a,i*o);for(let m=0;m<i;++m){let g=0;for(let y=0;y<c;++y)g+=e[m*c+y]*d[y];for(let y=0;y<o;++y)f[m*o+y]=Math.trunc(g/h[y]),g%=h[y]}return[f,[i,o],l]}function c3(e,t,a,n,r,s=!1,i=0){let o=n.length,l=[t[0],e.length/t[0]],u=l[1],p=o>0?r[o-1]+1:0;if(p<0)throw new Error(T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let c=t.slice();c[0]=p;let d=c.reduce((x,A)=>x*A,1),h=v.getArrayFromDType(a,d);if(o===0)return p>0&&h.fill(i),[h,c];if(p<=0)throw new Error(T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let f=0,m=1,g=0,y=r[f];for(;;){let x=0;if(m<o){if(x=r[m],y===x){++m;continue}if(y>=x)throw new Error(T.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(y<0||y>=p)throw new Error(T.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y,p));y>g&&h.fill(i,g*u,y*u);for(let A=f;A<m;++A){let b=n[A];if(b<0||b>=l[0])throw new Error(T.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(A,n[A],l[0]));for(let k=0;k<u;k++)h[y*u+k]+=e[b*u+k]}if(s)for(let A=0;A<u;A++)h[y*u+A]/=m-f;if(f=m,++m,g=y+1,y=x,m>o)break}return g<p&&h.fill(i,g*u,p*u),[h,c]}var SO=ls(e=>Math.sqrt(e)),TO=lt(so,e=>Math.sqrt(e)),CO={kernelName:so,backendName:"cpu",kernelFunc:TO},k7=Lt((e,t)=>{let a=e-t;return a*a}),NO=Yt(lo,k7),EO={kernelName:lo,backendName:"cpu",kernelFunc:NO};function w7(e,t,a,n){let r=_e(e,t.dtype);for(let s=0;s<r.size;s++){let i=r.indexToLoc(s),o=new Array(i.length);for(let l=0;l<o.length;l++)o[l]=i[l]*a[l]+n[l];r.set(t.get(...o),...i)}return r}var RO=class{constructor(e,t,a,n,r,s){this.separator=v.encodeString(e),this.nGramWidths=t,this.leftPad=v.encodeString(a),this.rightPad=v.encodeString(n),this.padWidth=r,this.preserveShort=s}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let a=this.getPadWidth(t);return Math.max(0,e+2*a-t+1)}createNGrams(e,t,a,n,r,s){for(let i=0;i<r;++i){let o=this.getPadWidth(s),l=Math.max(0,o-i),u=Math.max(0,o-(r-(i+1))),p=s-(l+u),c=t+(l>0?0:i-o),d=0;d+=l*this.leftPad.length;for(let y=0;y<p;++y)d+=e[c+y].length;d+=u*this.rightPad.length;let h=l+u+p-1;d+=h*this.separator.length,a[n+i]=new Uint8Array(d);let f=a[n+i],m=0,g=y=>y.forEach(x=>f[m++]=x);for(let y=0;y<l;++y)g(this.leftPad),g(this.separator);for(let y=0;y<p-1;++y)g(e[c+y]),g(this.separator);if(p>0){g(e[c+p-1]);for(let y=0;y<u;++y)g(this.separator),g(this.rightPad)}else{for(let y=0;y<u-1;++y)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let a=e.length,n=t.length;if(n>0){let o=t[0];if(o!==0)throw new Error(`First split value must be 0, got ${o}`);for(let l=1;l<n;++l){let u=t[l]>=o;if(u=u&&t[l]<=a,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${o}, ${a}]`);o=t[l]}if(o!==a)throw new Error(`Last split value must be data size. Expected ${a}, got ${o}`)}let r=n-1,s=v.getArrayFromDType("int32",n);if(a===0||n===0){let o=new Array(a);for(let l=0;l<=r;++l)s[l]=0;return[o,s]}s[0]=0;for(let o=1;o<=r;++o){let l=t[o]-t[o-1],u=0;this.nGramWidths.forEach(p=>{u+=this.getNumNGrams(l,p)}),this.preserveShort&&l>0&&u===0&&(u=1),s[o]=s[o-1]+u}let i=new Array(s[r]);for(let o=0;o<r;++o){let l=t[o],u=s[o];if(this.nGramWidths.forEach(p=>{let c=t[o+1]-t[o],d=this.getNumNGrams(c,p);this.createNGrams(e,l,i,u,d,p),u+=d}),this.preserveShort&&u===s[o]){let p=t[o+1]-t[o];if(p===0)continue;let c=p+2*this.padWidth,d=1;this.createNGrams(e,l,i,u,d,c)}}return[i,s]}};function h3(e,t,a,n,r,s,i,o){return new RO(a,n,r,s,i,o).compute(e,t)}function MO(e,t,a,n){if(!e.length)return;if(t.length===0){for(let s=0;s<e.length;++s)n.push(e.subarray(s,s+1));return}if(t.length===1){let s=t[0],i=e.indexOf(s);for(;i!==-1;){let o=e.subarray(0,i);(!a||o.length!==0)&&n.push(o),e=e.subarray(i+1),i=e.indexOf(s)}(!a||e.length!==0)&&n.push(e);return}let r=0;for(let s=0;s<e.length+1;s++)if(s===e.length||t.indexOf(e[s])!==-1){let i=e.subarray(r,s);(!a||i.length!==0)&&n.push(i),r=s+1}}function f3(e,t,a){let n=e.length,r=[],s=0,i=0,o=new Array(n);for(let d=0;d<n;++d){let h=r.length;MO(e[d],t,a,r);let f=r.length-h;o[d]=f,s+=f,i=Math.max(i,f)}let l=v.getArrayFromDType("int32",s*2),u=new Array(s),p=[n,i],c=0;for(let d=0;d<n;++d)for(let h=0;h<o[d];++h)l[c*2]=d,l[c*2+1]=h,u[c]=r[c],++c;return[l,u,p]}function m3(e,t){let a=v.getArrayFromDType("int32",e.length);for(let n=0;n<e.length;++n)a[n]=v.fingerPrint64(e[n]).modulo(t).getLowBitsUnsigned();return a}var I7=Lt((e,t)=>e-t),$O=i3((e,t,a,n)=>({real:e-a,imag:t-n})),g3=Yt(po,I7,$O),_O={kernelName:po,backendName:"cpu",kernelFunc:g3};function S7(e,t){let a=new Array(e.rank);for(let r=0;r<a.length;r++)a[r]=e.shape[r]*t[r];let n=_e(a,e.dtype);for(let r=0;r<n.values.length;++r){let s=n.indexToLoc(r),i=new Array(e.rank);for(let l=0;l<i.length;l++)i[l]=s[l]%e.shape[l];let 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BD(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n;Ae([r,s],"conv2dBackpropFilter");let c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,y=d.dataFormat==="channelsLast",x=new jt(d.filterShape,"float32"),A=d.padInfo.left,b=d.padInfo.top,k=a.data.get(r.dataId).values,S=a.data.get(s.dataId).values,C=new jt(r.shape,r.dtype,k),N=new jt(s.shape,s.dtype,S);for(let $=0;$<m;++$){let M=Math.max(0,Math.ceil((b-$)/h)),R=Math.min(d.outHeight,(d.inHeight+b-$)/h);for(let I=0;I<g;++I){let _=Math.max(0,Math.ceil((A-I)/f)),D=Math.min(d.outWidth,(d.inWidth+A-I)/f);for(let W=0;W<d.inChannels;++W)for(let P=0;P<d.outChannels;++P){let U=0;for(let G=0;G<d.batchSize;++G)for(let q=M;q<R;++q){let H=$+q*h-b;for(let B=_;B<D;++B){let Z=I+B*f-A;y?U+=C.get(G,H,Z,W)*N.get(G,q,B,P):U+=C.get(G,W,H,Z)*N.get(G,P,q,B)}}x.set(U,$,I,W,P)}}}return a.makeTensorInfo(x.shape,x.dtype,x.values)}var WD={kernelName:Cd,backendName:"cpu",kernelFunc:BD};function VD(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n;Ae([r,s],"conv2dBackpropInput");let c=v.computeStrides(s.shape),d=v.computeStrides(r.shape),h=T.convertConv2DDataFormat(u),f=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,h),m=new jt(f.inShape,"float32"),g=m.values,y=a.data.get(r.dataId).values,x=a.data.get(s.dataId).values,[A,b,k]=c,{batchSize:S,filterHeight:C,filterWidth:N,inChannels:$,inHeight:M,inWidth:R,outChannels:I,outHeight:_,outWidth:D,strideHeight:W,strideWidth:P}=f;h=f.dataFormat;let U=C-1-f.padInfo.top,G=N-1-f.padInfo.left,q=h==="channelsLast",H=m.strides[0],B=q?m.strides[1]:m.strides[2],Z=q?m.strides[2]:1,X=q?1:m.strides[1],ne=d[0],ee=q?d[1]:d[2],ce=q?d[2]:1,ie=q?1:d[1];for(let me=0;me<S;++me)for(let Se=0;Se<$;++Se)for(let Ne=0;Ne<M;++Ne){let 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u=v.computeStrides(r.shape),p=v.computeStrides(s.shape),c=T.computeConv3DInfo(r.shape,l,i,1,o),d=c.strideDepth,h=c.strideHeight,f=c.strideWidth,m=c.filterDepth,g=c.filterHeight,y=c.filterWidth,x=new jt(c.filterShape,"float32"),A=x.values,[b,k,S,C]=x.strides,N=a.data.get(s.dataId).values,[$,M,R,I]=p,_=a.data.get(r.dataId).values,[D,W,P,U]=u,G=c.padInfo.front,q=c.padInfo.left,H=c.padInfo.top;for(let B=0;B<m;++B){let Z=Math.max(0,Math.ceil((G-B)/d)),X=Math.min(c.outDepth,(c.inDepth+G-B)/d),ne=B*b;for(let ee=0;ee<g;++ee){let ce=Math.max(0,Math.ceil((H-ee)/h)),ie=Math.min(c.outHeight,(c.inHeight+H-ee)/h),me=ee*k+ne;for(let Se=0;Se<y;++Se){let Ne=Math.max(0,Math.ceil((q-Se)/f)),Be=Math.min(c.outWidth,(c.inWidth+q-Se)/f),qe=Se*S+me;for(let dt=0;dt<c.inChannels;++dt){let it=dt*C+qe;for(let at=0;at<c.outChannels;++at){let nt=0;for(let Ge=0;Ge<c.batchSize;++Ge){let ht=Ge*D,Ua=Ge*$;for(let Ot=Z;Ot<X;++Ot){let on=(B+Ot*d-G)*W+ht,ra=Ot*M+Ua;for(let _a=ce;_a<ie;++_a){let 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cz={kernelName:eh,backendName:"cpu",kernelFunc:pz};function hz(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n;Ae([r,s],"depthwiseConv2DNativeBackpropInput");let c=v.computeStrides(r.shape),d=v.computeStrides(s.shape),h=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),f=new jt(h.inShape,"float32"),m=f.values,[g,y,x]=f.strides,A=a.data.get(r.dataId).values,[b,k,S]=c,C=a.data.get(s.dataId).values,[N,$,M]=d,{batchSize:R,filterHeight:I,filterWidth:_,inChannels:D,inHeight:W,inWidth:P,outChannels:U,outHeight:G,outWidth:q,strideHeight:H,strideWidth:B}=h,Z=I-1-h.padInfo.top,X=_-1-h.padInfo.left,ne=U/D;for(let ee=0;ee<R;++ee)for(let ce=0;ce<D;++ce)for(let ie=0;ie<W;++ie){let me=ie-Z,Se=Math.max(0,Math.ceil(me/H)),Ne=Math.min(G,(I+me)/H);for(let Be=0;Be<P;++Be){let qe=Be-X,dt=Math.max(0,Math.ceil(qe/B)),it=Math.min(q,(_+qe)/B),at=0;for(let nt=Se;nt<Ne;++nt){let Ge=nt*H-me;for(let ht=dt;ht<it;++ht){let 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gz={kernelName:Ed,backendName:"cpu",kernelFunc:mz},yz={kernelName:Rd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:k,filterHeight:S,filterWidth:C,dilationHeight:N,dilationWidth:$,outShape:M}=T.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),R=v.sizeFromShape(M),I=M.length,_=v.getArrayFromDType(n.dtype,R);for(let D=0;D<h;++D)for(let W=0;W<y;++W){let P=W*b-A.top;for(let U=0;U<x;++U){let G=U*k-A.left;for(let q=0;q<g;++q){let H=Number.MIN_SAFE_INTEGER;for(let Z=0;Z<S;++Z){let X=P+Z*N;if(X>=0&&X<f)for(let ne=0;ne<C;++ne){let ee=G+ne*$;if(ee>=0&&ee<m){let ce=v.locToIndex([D,X,ee,q],p,v.computeStrides(n.shape)),ie=v.locToIndex([Z,ne,q],d,v.computeStrides(r.shape)),me=u[ce]+c[ie];me>H&&(H=me)}}}let 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M=v.toNestedArray($,u.data.get(s.dataId).values),R=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let I=0;I<d;++I)for(let _=0;_<g;++_){let D=_*A-x.top;for(let W=0;W<y;++W){let P=W*b-x.left;for(let U=0;U<m;++U){let G=Number.MIN_SAFE_INTEGER,q=D<0?0:D,H=P<0?0:P;for(let B=0;B<k;++B){let Z=D+B*C;if(Z>=0&&Z<h)for(let X=0;X<S;++X){let ne=P+X*N;if(ne>=0&&ne<f){let ee=p[I][Z][ne][U]+c[B][X][U];ee>G&&(G=ee,q=Z,H=ne)}}}R[I][q][H][U]+=M[I][_][W][U]}}}return{dataId:u.write(v.toTypedArray(R,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function dp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ae(r,"sum");let o;r.dtype==="bool"?o=Qr({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=er({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=T.getAxesPermutation(u,l),c=u,d=o;p!=null&&(d=Ba({inputs:{x:o},backend:a,attrs:{perm:p}}),c=T.getInnerMostAxes(c.length,l)),T.assertAxesAreInnerMostDims("sum",c,d.shape.length);let[h,f]=T.computeOutAndReduceShapes(d.shape,c),m=T.upcastType(d.dtype,"int32"),g=Pc(a,h,m),y=v.sizeFromShape(f),x=a.data.get(g.dataId).values,A=a.data.get(d.dataId).values;for(let b=0;b<x.length;++b){let k=b*y,S=0;for(let C=0;C<y;++C)S+=A[k+C];x[b]=S}if(i){let b=T.expandShapeToKeepDim(g.shape,u),k=g;g=mt({inputs:{x:g},backend:a,attrs:{shape:b}}),a.disposeIntermediateTensorInfo(k)}return a.disposeIntermediateTensorInfo(o),p!=null&&a.disposeIntermediateTensorInfo(d),g}var bz={kernelName:io,backendName:"cpu",kernelFunc:dp};function 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       bool isnan_custom(float val) {
         uint floatToUint = floatBitsToUint(val);
         return (floatToUint & 0x7fffffffu) > 0x7f800000u;
@@ -100,7 +100,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:a,varyingFs:n,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Ao(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / ${r}`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Mh(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function pV(e,t){let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}function cV(e,t,a="index"){let n=e.map((s,i)=>i),r=pV(n,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${a} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${a} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function k3(e){let t=v.computeStrides(e).map(a=>a.toString());return`
+    `),{version:e,attribute:t,varyingVs:a,varyingFs:n,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Ao(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / ${r}`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Rh(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function pV(e,t){let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}function cV(e,t,a="index"){let n=e.map((s,i)=>i),r=pV(n,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${a} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${a} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function k3(e){let t=v.computeStrides(e).map(a=>a.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
@@ -108,7 +108,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
-`}var y6=`
+`}var g6=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -147,10 +147,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
 
     return c / 255.0;
   }
-`,{getBroadcastDims:x6}=T;function hV(e,t,a){let n=[];if(e.forEach(d=>{let h=v.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`)),a.enableShapeUniforms){let{uniformShape:f}=I3(a.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${d.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${d.name}TexShape;`)}}),a.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;")}a.customUniforms&&a.customUniforms.forEach(d=>{n.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let r=n.join(`
+`,{getBroadcastDims:y6}=T;function hV(e,t,a){let n=[];if(e.forEach(d=>{let h=v.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`)),a.enableShapeUniforms){let{uniformShape:f}=I3(a.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${d.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${d.name}TexShape;`)}}),a.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;")}a.customUniforms&&a.customUniforms.forEach(d=>{n.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let r=n.join(`
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-`)}function du(e,t=!1){let a=e.shapeInfo.logicalShape;switch(a.length){case 0:return OV(e,t);case 1:return zV(e,t);case 2:return BV(e,t);case 3:return VV(e,t);case 4:return GV(e,t);case 5:return HV(e);case 6:return jV(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function A6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return FV(e);case 1:return DV(e,t);case 2:return LV(e,t);case 3:return WV(e,t);default:return UV(e,t)}}function fV(e,t,a=!1,n){let r="";a?r+=A6(e,n):r+=du(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=qV(e,t):r+=XV(e,t)),r}function mV(e,t,a){switch(e.length){case 0:return b6();case 1:return SV(e,t,a);case 2:return _V(e,t,a);case 3:return CV(e,t,a);default:return EV(e,t,a)}}function gV(e,t,a){switch(e.length){case 0:return b6();case 1:return TV(e,t,a);case 2:return PV(e,t,a);case 3:return NV(e,t,a);case 4:return RV(e,t,a);case 5:return MV(e,t);case 6:return $V(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function yV(e){return`
+`)}function du(e,t=!1){let a=e.shapeInfo.logicalShape;switch(a.length){case 0:return OV(e,t);case 1:return zV(e,t);case 2:return BV(e,t);case 3:return VV(e,t);case 4:return GV(e,t);case 5:return HV(e);case 6:return jV(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function x6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return FV(e);case 1:return DV(e,t);case 2:return LV(e,t);case 3:return WV(e,t);default:return UV(e,t)}}function fV(e,t,a=!1,n){let r="";a?r+=x6(e,n):r+=du(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=qV(e,t):r+=XV(e,t)),r}function mV(e,t,a){switch(e.length){case 0:return A6();case 1:return SV(e,t,a);case 2:return _V(e,t,a);case 3:return CV(e,t,a);default:return EV(e,t,a)}}function gV(e,t,a){switch(e.length){case 0:return A6();case 1:return TV(e,t,a);case 2:return PV(e,t,a);case 3:return NV(e,t,a);case 4:return RV(e,t,a);case 5:return MV(e,t);case 6:return $V(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function yV(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
@@ -260,7 +260,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function b6(){return`
+`;function A6(){return`
     int getOutputCoords() {
       return 0;
     }
@@ -357,7 +357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
     int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-    ${Mh(["r","c","d"],e)}
+    ${Rh(["r","c","d"],e)}
     return ivec3(r, c, d);
   }
 `;let n=Ao(["r","c","d"],e);return`
@@ -414,7 +414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
       int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      ${Mh(["r","c","d","d2"],e)}
+      ${Rh(["r","c","d","d2"],e)}
       return ivec4(r, c, d, d2);
     }
   `;let n=Ao(["r","c","d","d2"],e);return`
@@ -687,7 +687,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     return sampleTexture(${n}, uv);
   }
 `}function WV(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(a[0]===1){let d=a.slice(1),h=[1,2],f=cu(e,d),m=["b","row","col"];return`
-        ${A6(f,t)}
+        ${x6(f,t)}
         vec4 ${r}(int b, int row, int col) {
           return ${r}(${hu(m,h)});
         }
@@ -951,7 +951,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         return ${t}[i];
       }
     }
-  `}function qV(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=x6(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,p,c=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(`
+  `}function qV(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=y6(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,p,c=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(`
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       return vec4(outputValue.xy, outputValue.xy);
     `;else if(f&&!m)i===1?h=`
@@ -969,16 +969,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       float ${r}() {
         return sampleTexture(${a}, resultUV);
       }
-    `;let u=gt(l),p=x6(e.shapeInfo.logicalShape,t.logicalShape),c=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&p.length>=1?d="coords = 0;":d=p.map(m=>`coords.${h[m+c]} = 0;`).join(`
+    `;let u=gt(l),p=y6(e.shapeInfo.logicalShape,t.logicalShape),c=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&p.length>=1?d="coords = 0;":d=p.map(m=>`coords.${h[m+c]} = 0;`).join(`
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     float ${r}() {
       ${u} coords = getOutputCoords();
       ${d}
       return get${n}(${f});
     }
-  `}function gt(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 ${e} is not yet supported`)}function I3(e,t,a){let{newShape:n,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,a)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function cu(e,t){let a=JSON.parse(JSON.stringify(e));return a.shapeInfo.logicalShape=t,a}function hu(e,t){return t.map(a=>e[a]).join(", ")}function KV(e,t,a,n){let r=a.map((p,c)=>{let d={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(d.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[c],shapeInfo:d}}),s=r.map(p=>p.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=hV(r,i,t),l=Z7(e.gl,o),u=e.createProgram(l);return V().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},v6(e,t,u))}function v6(e,t,a){let n={},r={},s={},i=[],o,l,u,p=null,c=null;c=e.getUniformLocation(a,"NAN",!1),V().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(a,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];n[f]=e.getUniformLocation(a,f,d),n[`offset${f}`]=e.getUniformLocation(a,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(a,`${f}Shape`,d),s[`${f}TexShape`]=e.getUniformLocation(a,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(a,"outShape",d),u=e.getUniformLocation(a,"outShapeStrides",d),l=e.getUniformLocation(a,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(a,h.name,d)}),{uniformLocations:n,customUniformLocations:i,infLoc:p,nanLoc:c,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function dy(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((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${o} and ${l} must match`)})}function ZV(e,t,a,n,r){t.program.enableShapeUniforms||(dy(t.inShapeInfos,a),dy([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),V().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),a.forEach((l,u)=>{let p=t.program.variableNames[u],c=t.uniformLocations[p],d=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],f=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:m}=I3(t.program.packedInputs,l.shape,l.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,l.texData.texShape[0],l.texData.texShape[1]),c!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(c,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(c,m)}return}l.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,c,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=v.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&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],c=r[u];if(l.type==="float")e.gl.uniform1fv(p,c);else if(l.type==="vec2")e.gl.uniform2fv(p,c);else if(l.type==="vec3")e.gl.uniform3fv(p,c);else if(l.type==="vec4")e.gl.uniform4fv(p,c);else if(l.type==="int")e.gl.uniform1iv(p,c);else if(l.type==="ivec2")e.gl.uniform2iv(p,c);else if(l.type==="ivec3")e.gl.uniform3iv(p,c);else if(l.type==="ivec4")e.gl.uniform4iv(p,c);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function YV(e,t,a){let n="";t.concat(a).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=I3(e.packedInputs,i.shape,l),d="",h="",f="";if(p.length===1&&e.packedInputs){let k=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let k=v.computeStrides(p);f=`${k[0]===l[1]}_${k[k.length-1]===l[1]}`}let m=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,x=T.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&m===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${A}_${u?c:""}_${p.length}_${y}_${x}_${g}_${d}_${h}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${V().getNumber("WEBGL_VERSION")}`,s}function Ea(e){return V().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var JV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Na();this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length),this.userCode=`
+  `}function gt(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 ${e} is not yet supported`)}function I3(e,t,a){let{newShape:n,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,a)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function cu(e,t){let a=JSON.parse(JSON.stringify(e));return a.shapeInfo.logicalShape=t,a}function hu(e,t){return t.map(a=>e[a]).join(", ")}function KV(e,t,a,n){let r=a.map((p,c)=>{let d={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(d.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[c],shapeInfo:d}}),s=r.map(p=>p.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=hV(r,i,t),l=K7(e.gl,o),u=e.createProgram(l);return V().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},b6(e,t,u))}function b6(e,t,a){let n={},r={},s={},i=[],o,l,u,p=null,c=null;c=e.getUniformLocation(a,"NAN",!1),V().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(a,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];n[f]=e.getUniformLocation(a,f,d),n[`offset${f}`]=e.getUniformLocation(a,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(a,`${f}Shape`,d),s[`${f}TexShape`]=e.getUniformLocation(a,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(a,"outShape",d),u=e.getUniformLocation(a,"outShapeStrides",d),l=e.getUniformLocation(a,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(a,h.name,d)}),{uniformLocations:n,customUniformLocations:i,infLoc:p,nanLoc:c,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function uy(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((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(a.isUniform&&s.isUniform)return;let o=a.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function ZV(e,t,a,n,r){t.program.enableShapeUniforms||(uy(t.inShapeInfos,a),uy([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),V().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),a.forEach((l,u)=>{let p=t.program.variableNames[u],c=t.uniformLocations[p],d=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],f=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:m}=I3(t.program.packedInputs,l.shape,l.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,l.texData.texShape[0],l.texData.texShape[1]),c!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(c,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(c,m)}return}l.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,c,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=v.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&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],c=r[u];if(l.type==="float")e.gl.uniform1fv(p,c);else if(l.type==="vec2")e.gl.uniform2fv(p,c);else if(l.type==="vec3")e.gl.uniform3fv(p,c);else if(l.type==="vec4")e.gl.uniform4fv(p,c);else if(l.type==="int")e.gl.uniform1iv(p,c);else if(l.type==="ivec2")e.gl.uniform2iv(p,c);else if(l.type==="ivec3")e.gl.uniform3iv(p,c);else if(l.type==="ivec4")e.gl.uniform4iv(p,c);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function YV(e,t,a){let n="";t.concat(a).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=I3(e.packedInputs,i.shape,l),d="",h="",f="";if(p.length===1&&e.packedInputs){let k=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let k=v.computeStrides(p);f=`${k[0]===l[1]}_${k[k.length-1]===l[1]}`}let m=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,x=T.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&m===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${A}_${u?c:""}_${p.length}_${y}_${x}_${g}_${d}_${h}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${V().getNumber("WEBGL_VERSION")}`,s}function Ea(e){return V().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var JV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=md.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Na();this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?Mh(["r","c","d"],e):Ao(["r","c","d"],e)}
+        ${this.enableShapeUniforms?Rh(["r","c","d"],e):Ao(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -996,9 +996,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},QV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=gd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Na();this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length),this.userCode=`
+    `}},QV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=md.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Na();this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?Mh(["r","c","d"],e):Ao(["r","c","d"],e)}
+        ${this.enableShapeUniforms?Rh(["r","c","d"],e):Ao(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1017,21 +1017,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${t.output} = result;
       }
     `}},eU=class{constructor(e){this.variableNames=["A"],this.outTexUsage=cn.DOWNLOAD;let t=Na();this.outputShape=e,this.userCode=`
-      ${y6}
+      ${g6}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
     `}},tU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=cn.DOWNLOAD;let t=Na();this.outputShape=e,this.userCode=`
-      ${y6}
+      ${g6}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},aU={R:0,G:1,B:2,A:3},py=class{constructor(e,t=!1,a="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Na();this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;i<a.length;i++){let o=a[i];s+=`
+    `}},aU={R:0,G:1,B:2,A:3},dy=class{constructor(e,t=!1,a="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Na();this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;i<a.length;i++){let o=a[i];s+=`
           if(offset == ${i}) {
             result = values[${aU[o]}];
           }`}this.userCode=`
@@ -1098,7 +1098,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
           ${a.output} = ${r};
         }
-    `}},k6={};Ze(k6,{bindVertexProgramAttributeStreams:()=>M6,createBufferFromOutputTexture:()=>P6,createFloat16MatrixTexture:()=>C6,createFloat16PackedMatrixTexture:()=>R6,createFloat32MatrixTexture:()=>T6,createIndexBuffer:()=>S6,createPackedMatrixTexture:()=>E6,createUnsignedBytesMatrixTexture:()=>N6,createVertexBuffer:()=>I6,createVertexShader:()=>w6,downloadByteEncodedFloatMatrixFromOutputTexture:()=>O6,downloadFloat32MatrixFromBuffer:()=>F6,downloadMatrixFromPackedOutputTexture:()=>z6,downloadPackedMatrixFromBuffer:()=>D6,getInternalFormatForFloat16MatrixTexture:()=>T3,getInternalFormatForFloat16PackedMatrixTexture:()=>E3,getInternalFormatForFloat32MatrixTexture:()=>S3,getInternalFormatForPackedMatrixTexture:()=>N3,getInternalFormatForUnsignedBytesMatrixTexture:()=>C3,uploadDenseMatrixToTexture:()=>$6,uploadPixelDataToTexture:()=>_6});function w6(e){let t=Na(),a=`${t.version}
+    `}},v6={};Ze(v6,{bindVertexProgramAttributeStreams:()=>R6,createBufferFromOutputTexture:()=>_6,createFloat16MatrixTexture:()=>T6,createFloat16PackedMatrixTexture:()=>E6,createFloat32MatrixTexture:()=>S6,createIndexBuffer:()=>I6,createPackedMatrixTexture:()=>N6,createUnsignedBytesMatrixTexture:()=>C6,createVertexBuffer:()=>w6,createVertexShader:()=>k6,downloadByteEncodedFloatMatrixFromOutputTexture:()=>F6,downloadFloat32MatrixFromBuffer:()=>P6,downloadMatrixFromPackedOutputTexture:()=>D6,downloadPackedMatrixFromBuffer:()=>O6,getInternalFormatForFloat16MatrixTexture:()=>T3,getInternalFormatForFloat16PackedMatrixTexture:()=>E3,getInternalFormatForFloat32MatrixTexture:()=>S3,getInternalFormatForPackedMatrixTexture:()=>N3,getInternalFormatForUnsignedBytesMatrixTexture:()=>C3,uploadDenseMatrixToTexture:()=>M6,uploadPixelDataToTexture:()=>$6});function k6(e){let t=Na(),a=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -1107,7 +1107,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return K7(e,a)}function I6(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 Q7(e,t)}function S6(e){let t=new Uint16Array([0,1,2,2,1,3]);return e6(e,t)}function hp(e,t,a,n,r,s){a6(t,a);let i=t6(e),o=e.TEXTURE_2D;return ue(e,()=>e.bindTexture(o,i)),ue(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ue(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ue(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ue(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),V().getNumber("WEBGL_VERSION")===1?ue(e,()=>e.texImage2D(o,0,n,t,a,0,r,s,null)):ue(e,()=>e.texStorage2D(o,1,n,t,a)),ue(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[a,t]}}function S3(e){return e.internalFormatFloat}function T6(e,t,a,n){let[r,s]=cp(t,a);return hp(e,r,s,S3(n),n.textureFormatFloat,e.FLOAT)}function T3(e){return e.internalFormatHalfFloat}function C6(e,t,a,n){let[r,s]=cp(t,a);return hp(e,r,s,T3(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function C3(e){return e.downloadTextureFormat}function N6(e,t,a,n){let[r,s]=cp(t,a);return hp(e,r,s,C3(n),e.RGBA,e.UNSIGNED_BYTE)}function N3(e){return e.internalFormatPackedFloat}function E6(e,t,a,n){let[r,s]=lu(t,a);return hp(e,r,s,N3(n),e.RGBA,e.FLOAT)}function E3(e){return e.internalFormatPackedHalfFloat}function R6(e,t,a,n){let[r,s]=lu(t,a);return hp(e,r,s,E3(n),e.RGBA,n.textureTypeHalfFloat)}function M6(e,t,a){return ue(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),x2(e,t,"clipSpacePos",a,3,20,0)&&x2(e,t,"uv",a,2,20,12)}function $6(e,t,a,n,r,s){ue(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(a*n*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new 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ue(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ue(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,0)),ue(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function F6(e,t,a){let n=e,r=new Float32Array(a);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,r),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),r}function O6(e,t,a,n){let[r,s]=cp(t,a),i=4,o=new Uint8Array(QW(t*a,i));return ue(e,()=>e.readPixels(0,0,r,s,n.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function D6(e,t,a,n,r,s,i,o){let l=e,u=new Float32Array(eV(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function z6(e,t,a){let n=new Float32Array(t*a*4);return ue(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,n)),n}var sl=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=V().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,Rh(t,e)):this.gl=Dn(t),e=this.gl,V().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>ue(r,()=>r.createVertexArray()),this.bindVertexArray=s=>ue(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>ue(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>ue(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ue(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>ue(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>ue(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>ue(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let a="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),V().getNumber("WEBGL_VERSION")===1){let 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floats");this.vertexBuffer=I6(this.gl),this.indexBuffer=S6(this.gl),this.framebuffer=n6(this.gl),this.textureConfig=b3(this.gl,this.textureHalfFloatExtension)}get debug(){return V().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;ue(e,()=>e.finish()),ue(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ue(e,()=>e.deleteFramebuffer(this.framebuffer)),ue(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ue(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ue(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),T6(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),C6(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),N6(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),_6(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,a,n){this.throwIfDisposed(),$6(this.gl,e,t,a,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return 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t=V().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,Eh(t,e)):this.gl=Dn(t),e=this.gl,V().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>ue(r,()=>r.createVertexArray()),this.bindVertexArray=s=>ue(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>ue(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>ue(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ue(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>ue(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>ue(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>ue(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let a="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),V().getNumber("WEBGL_VERSION")===1){let 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this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,a){this.throwIfDisposed(),this.throwIfNoProgram(),i6(this.gl,e,t,a)}setOutputMatrixTexture(e,t,a){this.setOutputMatrixTextureDriver(e,a,t)}setOutputPackedMatrixTexture(e,t,a){this.throwIfDisposed();let[n,r]=lu(t,a);this.setOutputMatrixTextureDriver(e,n,r)}setOutputMatrixWriteRegion(e,t,a,n){this.setOutputMatrixWriteRegionDriver(a,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,a,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Ac(this.gl,this.program),qu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ue(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ue(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ju(this.gl,V().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(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.createQuery();return a.beginQuery(n.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,a=this.getQueryTimerExtensionWebGL2();t.endQuery(a.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let a=this.gl;return a.getQueryParameter(e,a.QUERY_RESULT)/1e6}else{let a=this.getQueryTimerExtensionWebGL1();return a.getQueryObjectEXT(e,a.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.getQueryParameter(e,a.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let a=this.getQueryTimerExtensionWebGL1(),n=a.getQueryObjectEXT(e,a.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=rU(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:a}=this.itemsToPoll[t];a()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let a;"setTimeoutCustom"in V().platform&&(a=V().platform.setTimeoutCustom.bind(V().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,a)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),bc(this.gl,e,this.framebuffer),this.debug&&qu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bc(this.gl,this.outputTexture,this.framebuffer),this.debug&&qu(this.gl)):A2(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let a=t();return this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;bc(n,e,this.framebuffer),this.debug&&qu(n),this.outputTexture=e,ue(n,()=>n.viewport(0,0,t,a)),ue(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),ue(this.gl,()=>this.gl.scissor(e,t,a,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 rU(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:sU,bincountImpl:z6,bincountReduceImpl:iU,castImpl:oU,ceilImpl:lU,concatImpl:uU,equalImpl:dU,expImpl:pU,expm1Impl:cU,floorImpl:hU,gatherNdImpl:fU,gatherV2Impl:mU,greaterImpl:gU,greaterEqualImpl:yU,lessImpl:xU,lessEqualImpl:AU,linSpaceImpl:bU,logImpl:vU,maxImpl:kU,maximumImpl:wU,minimumImpl:IU,multiplyImpl:SU,negImpl:TU,notEqualImpl:CU,prodImpl:NU,raggedGatherImpl:EU,raggedRangeImpl:RU,raggedTensorToTensorImpl:MU,rangeImpl:$U,rsqrtImpl:_U,scatterImpl:PU,sigmoidImpl:FU,simpleAbsImpl:L6,sliceImpl:OU,sparseFillEmptyRowsImpl:DU,sparseReshapeImpl:zU,sparseSegmentReductionImpl:B6,sqrtImpl:LU,stridedSliceImpl:BU,stringNGramsImpl:WU,stringSplitImpl:VU,stringToHashBucketFastImpl:UU,subImpl:GU,tileImpl:HU,topKImpl:jU,transposeImpl:R3,uniqueImpl:qU}=Ch;function W6(e,t){return["x","y","z","w","u","v"].slice(0,t).map(a=>`${e}.${a}`)}function ka(e,t){return t===1?[e]:W6(e,t)}function XU(e,t){if(e===1)return"rc";let a="";for(let n=0;n<e;n++)a+=t[n],n<e-1&&(a+=",");return a}var KU=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Ea(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -1134,7 +1134,7 @@ 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(${t[3]})`}},U6=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=`
+            rEdge || cEdge ? 0. : getA(${t[3]})`}},V6=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=`
         ${r}
         ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -1167,7 +1167,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${t?cV(["r","c","d"],"inputShape"):Ao(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var YU=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,a){let n=hy(t,a),r=fy(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=cy(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return n===ia.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===ia.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===ia.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===ia.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===ia.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=hy(a,n),s=fy(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=cy(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=V().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 JU(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function cy(e,t,a,n,r){let s=QU(t,n),i;if(r){let[l,u]=lu(e[0],e[1]);i=l*u}else{let[l,u]=cp(e[0],e[1]);i=l*u}let o=JU(a,s);return i*o}function QU(e,t){switch(e){case ia.PACKED_2X2_FLOAT32:return N3(t);case ia.PACKED_2X2_FLOAT16:return E3(t);case ia.UNPACKED_FLOAT32:return S3(t);case ia.UNPACKED_FLOAT16:return T3(t);case ia.PACKED_4X1_UNSIGNED_BYTE:return C3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function eG(e){return V().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?ia.PACKED_2X2_FLOAT32:ia.UNPACKED_FLOAT32:e?ia.PACKED_2X2_FLOAT16:ia.UNPACKED_FLOAT16}function hy(e,t){if(e===cn.UPLOAD)return ia.PACKED_2X2_FLOAT32;if(e===cn.RENDER||e==null)return eG(t);if(e===cn.DOWNLOAD||e===cn.PIXELS)return ia.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function fy(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var qn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length),this.userCode=`
+  `}var YU=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,a){let n=cy(t,a),r=hy(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=py(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return n===ia.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===ia.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===ia.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===ia.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===ia.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=cy(a,n),s=hy(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=py(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=V().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 JU(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function py(e,t,a,n,r){let s=QU(t,n),i;if(r){let[l,u]=lu(e[0],e[1]);i=l*u}else{let[l,u]=pp(e[0],e[1]);i=l*u}let o=JU(a,s);return i*o}function QU(e,t){switch(e){case ia.PACKED_2X2_FLOAT32:return N3(t);case ia.PACKED_2X2_FLOAT16:return E3(t);case ia.UNPACKED_FLOAT32:return S3(t);case ia.UNPACKED_FLOAT16:return T3(t);case ia.PACKED_4X1_UNSIGNED_BYTE:return C3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function eG(e){return V().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?ia.PACKED_2X2_FLOAT32:ia.UNPACKED_FLOAT32:e?ia.PACKED_2X2_FLOAT16:ia.UNPACKED_FLOAT16}function cy(e,t){if(e===cn.UPLOAD)return ia.PACKED_2X2_FLOAT32;if(e===cn.RENDER||e==null)return eG(t);if(e===cn.DOWNLOAD||e===cn.PIXELS)return ia.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function hy(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var qn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Ea(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1178,7 +1178,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},Nn="if (isnan(x)) return x;",tG="return x;",my="return abs(x);",aG="return (x >= 0.0) ? x : (exp(x) - 1.0);",nG=Nn+`
+    `}},Nn="if (isnan(x)) return x;",tG="return x;",fy="return abs(x);",aG="return (x >= 0.0) ? x : (exp(x) - 1.0);",nG=Nn+`
   return (x < 0.0) ? 0.0 : x;
 `,rG=Nn+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
@@ -1229,7 +1229,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},cG=Cn.whereImpl,hG=1e-7,fG=1e-4,Fm={};function mG(e){return e in Fm||(Fm[e]={}),Fm[e]}var gG=V().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),yG=600;function xG(){return V().global.screen==null?1024:V().global.screen.height*V().global.screen.width*window.devicePixelRatio*yG/1024/1024}var fu=class extends Al{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!V().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof sl)t=e;else{let a=Dn(V().getNumber("WEBGL_VERSION"),e);t=new sl(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=Dn(V().getNumber("WEBGL_VERSION"));t=new sl(a),this.binaryCache=mG(V().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new YU(this.gpgpu),this.numMBBeforeWarning=xG(),this.texData=new wd(this,vt())}nextDataId(){return fu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,a,n,r,s){let i=this.makeTensorInfo(t,a),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[n,r]},o.texShape=[n,r];let l=Ku(t),u=new py(l,!1,s),p=this.runWebGLProgram(u,[i],a,[[n,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,a){if((V().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||V().getBool("DEBUG"))&&this.checkNumericalProblems(e),a==="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:a,values:e,usage:cn.UPLOAD,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,a,n,r){if(V().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:a,dtype:n,values:t,usage:cn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:a,dtype:n,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let c;o?c=new Vr(i,Dr):c=new qn(i,Dr);let d=this.runWebGLProgram(c,[{dataId:e,shape:i,dtype:n}],n),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(a!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return a;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(n==="complex64"){let c=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);p=T.mergeRealAndImagArrays(c,d)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}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:a,shape:n,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Vr(n,Dr):h=new qn(n,Dr);let f=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(a!=null)return this.convertAndCacheOnCPU(e);if(V().getBool("DEBUG")&&!V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&V().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"&&V().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...hc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];p=T.mergeRealAndImagArrays(f,m)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ue(h,()=>h.deleteBuffer(l))}let c=this.convertAndCacheOnCPU(e,p),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(c)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&vt().removeDataId(e,this),this.pendingDeletes--),c}readToGPU(e,t={}){let a=this.texData.get(e),{values:n,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=a;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let d;o?d=new Vr(r,Dr):d=new qn(r,Dr);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),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),p=vt().makeTensorFromTensorInfo(u),c=this.texData.get(u.dataId);return Object.assign({tensorRef:p},c.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return _e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let a=e[t];if(!q7(a))throw V().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${a} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${a} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:a,isPacked:n}=this.texData.get(e),r=v.sizeFromShape(t);if(V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let c=this.decode(e),d=this.texData.get(c.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...hc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(c),h}let s=V().getBool("WEBGL_PACK")&&n===!0,i=s?Ku(t):t,o=s?new tU(i):new eU(i),l=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],"float32"),u=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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 V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(V().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:a}=this.texData.get(e);return a!=null&&(this.disposeData(a.real.dataId,t),this.disposeData(a.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:a,texShape:n,usage:r,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,a),this.textureManager.releaseTexture(t,n,r,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=gG){return V().getBool("WEBGL_CPU_FORWARD")&&e.every(a=>this.texData.get(a.dataId).texture==null&&v.sizeFromShape(a.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){T.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return cG(e.shape,t)}packedUnaryOp(e,t,a){let n=new Vr(e.shape,t),r=this.compileAndRun(n,[e],a);return vt().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=B6(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(V().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,my,e.dtype);let t=new qn(e.shape,my),a=this.compileAndRun(t,[e]);return vt().makeTensorFromTensorInfo(a)}makeTensorInfo(e,t,a){let n;if(t==="string"&&a!=null&&a.length>0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,a){return vt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,a),this)}unpackTensor(e){let t=new pG(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new KU(e.shape),a=!0;return this.runWebGLProgram(t,[e],e.dtype,null,a)}packedReshape(e,t){let a=[Hs(e.shape),...js(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},r=[Hs(t),...js(t)],s=new U6(r,a),i=!0,o=[a],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let a=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=a;if(t!=null){let c=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(c<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Ku(r),o;n?o=new QV(i):o=new JV(i);let l=!0,u=[t!=null?t:hc(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,a,n,r=!1,s){let i=this.makeTensorInfo(e.outputShape,a),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===gd.DENSE){let g=s!=null?s:hc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.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&&v.sizeFromShape(g.shape)<=V().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&&!yd(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},c=YV(e,u,p),d=this.getAndSaveBinary(c,()=>KV(this.gpgpu,e,u,p)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),V().get("ENGINE_COMPILE_ONLY")||ZV(this.gpgpu,d,u,p,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=V().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!V().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,a,n,r=!1){return a=a||t[0].dtype,this.runWebGLProgram(e,t,a,n,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(V().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=Fe(()=>{if(!V().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=V().getBool("DEBUG");V().set("DEBUG",!1);let t=this.abs(ze(1e-8)).dataSync()[0];if(V().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hG:fG}uploadToGPU(e){let t=this.texData.get(e),{shape:a,dtype:n,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=d6(a,o),t.texShape=p),r!=null){let c=Ku(a),d,h=p[1],f=p[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=lu(p[0],p[1])),o?d=new nU(c,m):d=new py(c,m);let g=m?[f,h]:p,y=this.makeTensorInfo(g,n),x=this.texData.get(y.dataId);m?x.usage=cn.PIXELS:x.usage=cn.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let A=[[f,h]],b=!0,k=this.runWebGLProgram(d,[y],n,A,b),S=this.texData.get(k.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,V().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let c=this.acquireTexture(p,i,n,o);t.texture=c}}convertAndCacheOnCPU(e,t){let a=this.texData.get(e),{dtype:n}=a;return t!=null&&(a.values=AG(t,n)),a.values}acquireTexture(e,t,a,n){if(this.numBytesInGPU+=this.computeBytes(e,a),!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,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let a=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(r){throw r}});e.push(a)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await b4(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(v3(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:a,infLoc:n,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=v6(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=a,e.infLoc=n,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromGPUData(e,t,a){e.channels=e.channels||"RGBA";let{texture:n,height:r,width:s,channels:i}=e,o=vt().backend;if(!o.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(n,t,a,r,s,i);return vt().makeTensorFromDataId(l,t,a,o)}};fu.nextDataId=0;function AG(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<a.length;++n)a[n]=Math.round(e[n]);return a}else throw new Error(`Unknown dtype ${t}`)}var bG="4.2.0";function G6(){V().set("WEBGL_FORCE_F16_TEXTURES",!0)}Zd.isBrowser()&&yo("webgl",()=>new fu,2);var vG={forceHalfFloat:G6},M3=`
+    `}},cG=Cn.whereImpl,hG=1e-7,fG=1e-4,Fm={};function mG(e){return e in Fm||(Fm[e]={}),Fm[e]}var gG=V().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),yG=600;function xG(){return V().global.screen==null?1024:V().global.screen.height*V().global.screen.width*window.devicePixelRatio*yG/1024/1024}var fu=class extends Al{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!V().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof sl)t=e;else{let a=Dn(V().getNumber("WEBGL_VERSION"),e);t=new sl(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=Dn(V().getNumber("WEBGL_VERSION"));t=new sl(a),this.binaryCache=mG(V().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new YU(this.gpgpu),this.numMBBeforeWarning=xG(),this.texData=new kd(this,vt())}nextDataId(){return fu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,a,n,r,s){let i=this.makeTensorInfo(t,a),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[n,r]},o.texShape=[n,r];let l=Xu(t),u=new dy(l,!1,s),p=this.runWebGLProgram(u,[i],a,[[n,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,a){if((V().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||V().getBool("DEBUG"))&&this.checkNumericalProblems(e),a==="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:a,values:e,usage:cn.UPLOAD,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,a,n,r){if(V().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:a,dtype:n,values:t,usage:cn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:a,dtype:n,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let c;o?c=new Vr(i,Dr):c=new qn(i,Dr);let d=this.runWebGLProgram(c,[{dataId:e,shape:i,dtype:n}],n),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(a!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return a;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(n==="complex64"){let c=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);p=T.mergeRealAndImagArrays(c,d)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}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:a,shape:n,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Vr(n,Dr):h=new qn(n,Dr);let f=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(a!=null)return this.convertAndCacheOnCPU(e);if(V().getBool("DEBUG")&&!V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&V().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"&&V().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...cc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];p=T.mergeRealAndImagArrays(f,m)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ue(h,()=>h.deleteBuffer(l))}let c=this.convertAndCacheOnCPU(e,p),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(c)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&vt().removeDataId(e,this),this.pendingDeletes--),c}readToGPU(e,t={}){let a=this.texData.get(e),{values:n,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=a;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let d;o?d=new Vr(r,Dr):d=new qn(r,Dr);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),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),p=vt().makeTensorFromTensorInfo(u),c=this.texData.get(u.dataId);return Object.assign({tensorRef:p},c.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return _e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let a=e[t];if(!j7(a))throw V().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${a} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${a} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:a,isPacked:n}=this.texData.get(e),r=v.sizeFromShape(t);if(V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let c=this.decode(e),d=this.texData.get(c.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...cc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(c),h}let s=V().getBool("WEBGL_PACK")&&n===!0,i=s?Xu(t):t,o=s?new tU(i):new eU(i),l=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],"float32"),u=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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 V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(V().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:a}=this.texData.get(e);return a!=null&&(this.disposeData(a.real.dataId,t),this.disposeData(a.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:a,texShape:n,usage:r,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,a),this.textureManager.releaseTexture(t,n,r,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=gG){return V().getBool("WEBGL_CPU_FORWARD")&&e.every(a=>this.texData.get(a.dataId).texture==null&&v.sizeFromShape(a.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){T.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return cG(e.shape,t)}packedUnaryOp(e,t,a){let n=new Vr(e.shape,t),r=this.compileAndRun(n,[e],a);return vt().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=L6(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(V().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,fy,e.dtype);let t=new qn(e.shape,fy),a=this.compileAndRun(t,[e]);return vt().makeTensorFromTensorInfo(a)}makeTensorInfo(e,t,a){let n;if(t==="string"&&a!=null&&a.length>0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,a){return vt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,a),this)}unpackTensor(e){let t=new pG(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new KU(e.shape),a=!0;return this.runWebGLProgram(t,[e],e.dtype,null,a)}packedReshape(e,t){let a=[Hs(e.shape),...js(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},r=[Hs(t),...js(t)],s=new V6(r,a),i=!0,o=[a],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let a=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=a;if(t!=null){let c=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(c<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Xu(r),o;n?o=new QV(i):o=new JV(i);let l=!0,u=[t!=null?t:cc(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,a,n,r=!1,s){let i=this.makeTensorInfo(e.outputShape,a),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===md.DENSE){let g=s!=null?s:cc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.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&&v.sizeFromShape(g.shape)<=V().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&&!gd(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},c=YV(e,u,p),d=this.getAndSaveBinary(c,()=>KV(this.gpgpu,e,u,p)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),V().get("ENGINE_COMPILE_ONLY")||ZV(this.gpgpu,d,u,p,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=V().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!V().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,a,n,r=!1){return a=a||t[0].dtype,this.runWebGLProgram(e,t,a,n,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(V().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=Oe(()=>{if(!V().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=V().getBool("DEBUG");V().set("DEBUG",!1);let t=this.abs(ze(1e-8)).dataSync()[0];if(V().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hG:fG}uploadToGPU(e){let t=this.texData.get(e),{shape:a,dtype:n,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=u6(a,o),t.texShape=p),r!=null){let c=Xu(a),d,h=p[1],f=p[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=lu(p[0],p[1])),o?d=new nU(c,m):d=new dy(c,m);let g=m?[f,h]:p,y=this.makeTensorInfo(g,n),x=this.texData.get(y.dataId);m?x.usage=cn.PIXELS:x.usage=cn.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let A=[[f,h]],b=!0,k=this.runWebGLProgram(d,[y],n,A,b),S=this.texData.get(k.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,V().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let c=this.acquireTexture(p,i,n,o);t.texture=c}}convertAndCacheOnCPU(e,t){let a=this.texData.get(e),{dtype:n}=a;return t!=null&&(a.values=AG(t,n)),a.values}acquireTexture(e,t,a,n){if(this.numBytesInGPU+=this.computeBytes(e,a),!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,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let a=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(r){throw r}});e.push(a)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await A4(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(v3(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:a,infLoc:n,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=b6(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=a,e.infLoc=n,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromGPUData(e,t,a){e.channels=e.channels||"RGBA";let{texture:n,height:r,width:s,channels:i}=e,o=vt().backend;if(!o.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(n,t,a,r,s,i);return vt().makeTensorFromDataId(l,t,a,o)}};fu.nextDataId=0;function AG(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<a.length;++n)a[n]=Math.round(e[n]);return a}else throw new Error(`Unknown dtype ${t}`)}var bG="4.2.0";function U6(){V().set("WEBGL_FORCE_F16_TEXTURES",!0)}Kd.isBrowser()&&yo("webgl",()=>new fu,2);var vG={forceHalfFloat:U6},M3=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,yl=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=Ea(this.outputShape.length),this.userCode=`
@@ -1242,12 +1242,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},fp=`
+    `}},hp=`
   result.r = isNaN.r ? NAN : result.r;
   result.g = isNaN.g ? NAN : result.g;
   result.b = isNaN.b ? NAN : result.b;
   result.a = isNaN.a ? NAN : result.a;
-`,mp=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=Ea(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=`
+`,fp=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=Ea(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
@@ -1291,13 +1291,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Ya(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var kG={kernelName:wi,backendName:"webgl",kernelFunc:Ya};function us(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=Ya({inputs:{x:n},backend:a}),l=Ya({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var wG={kernelName:Cd,backendName:"webgl",kernelFunc:us},H6="return (a < 0.) ? b * a : a;",j6=`
+    `}};function Ya(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var kG={kernelName:wi,backendName:"webgl",kernelFunc:Ya};function us(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=Ya({inputs:{x:n},backend:a}),l=Ya({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var wG={kernelName:Td,backendName:"webgl",kernelFunc:us},G6="return (a < 0.) ? b * a : a;",H6=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function IG(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new mp(j6,r.shape,i.shape):new yl(H6,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var SG={kernelName:Si,backendName:"webgl",kernelFunc:IG},q6="return (a < 0.) ? b * a : a;",X6=`
+`;function IG(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new fp(H6,r.shape,i.shape):new yl(G6,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var SG={kernelName:Si,backendName:"webgl",kernelFunc:IG},j6="return (a < 0.) ? b * a : a;",q6=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function TG(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new mp(X6,n.shape,r.shape):new yl(q6,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var CG={kernelName:ji,backendName:"webgl",kernelFunc:TG},mu="if (isnan(x)) return x;";function Qe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=V().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Vr(i.shape,t):p=new qn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function da({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let f=p.texData.get(l.dataId),m=p.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,k]=A,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:k.dataId,dtype:k.dtype,shape:u.shape},N=new yl(e,l.shape,u.shape);return p.runWebGLProgram(N,[S,C],fa(b.dtype,k.dtype))}),x=us({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),x}let c=s||fa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let f=p.texData.get(l.dataId).values,m=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(f):f,y=l.dtype==="string"?T.fromUint8ToStringArray(m):m,[x,A]=r(l.shape,u.shape,g,y,c),b=p.makeTensorInfo(A,c),k=p.texData.get(b.dataId);return k.values=x,b}let d=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new mp(t,l.shape,u.shape,a):h=new yl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function xd(e,t=!1){if(e==="linear")return t?iG:tG;if(e==="relu")return t?lG:nG;if(e==="elu")return t?oG:aG;if(e==="relu6")return t?uG:rG;if(e==="prelu")return t?X6:q6;if(e==="leakyrelu")return t?j6:H6;if(e==="sigmoid")return t?dG:sG;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var K6=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=Ea(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["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 TG(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new fp(q6,n.shape,r.shape):new yl(j6,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var CG={kernelName:ji,backendName:"webgl",kernelFunc:TG},mu="if (isnan(x)) return x;";function Qe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=V().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Vr(i.shape,t):p=new qn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function da({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let f=p.texData.get(l.dataId),m=p.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,k]=A,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:k.dataId,dtype:k.dtype,shape:u.shape},N=new yl(e,l.shape,u.shape);return p.runWebGLProgram(N,[S,C],fa(b.dtype,k.dtype))}),x=us({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),x}let c=s||fa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let f=p.texData.get(l.dataId).values,m=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(f):f,y=l.dtype==="string"?T.fromUint8ToStringArray(m):m,[x,A]=r(l.shape,u.shape,g,y,c),b=p.makeTensorInfo(A,c),k=p.texData.get(b.dataId);return k.values=x,b}let d=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new fp(t,l.shape,u.shape,a):h=new yl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function yd(e,t=!1){if(e==="linear")return t?iG:tG;if(e==="relu")return t?lG:nG;if(e==="elu")return t?oG:aG;if(e==="relu6")return t?uG:rG;if(e==="prelu")return t?q6:j6;if(e==="leakyrelu")return t?H6:G6;if(e==="sigmoid")return t?dG:sG;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var X6=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=Ea(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["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}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1336,7 +1336,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},gy={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},yy=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.userCode=`
+    `}},my={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},gy=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1349,7 +1349,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},xy="return a * b;";function $3(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=T.upcastType(n.dtype,r.dtype);if(n.dtype==="complex64"){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),u=new yy(gy.REAL,n.shape,r.shape),p=new yy(gy.IMAG,n.shape,r.shape),c=[{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:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=us({inputs:{real:d,imag:h},backend:a});return a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}if(a.shouldExecuteOnCPU([n,r])){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),[u,p]=SU(n.shape,r.shape,o.values,l.values,s),c=a.makeTensorInfo(p,s),d=a.texData.get(c.dataId);return d.values=u,c}let i;return V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new mp(xy,n.shape,r.shape):i=new yl(xy,n.shape,r.shape),a.runWebGLProgram(i,[n,r],s)}var NG={kernelName:Li,backendName:"webgl",kernelFunc:$3};function EG(e,t,a){let n=[Hs(e.shape),...js(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Hs(t),...js(t)],i=new U6(s,n),o=!0,l=[n],u=a.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{shape:s}=n,i=a,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!yd(r.shape,l)&&!(p.texture!==null&&yd(p.shape,l))?EG(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var RG={kernelName:Hl,backendName:"webgl",kernelFunc:pe},Ay=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=`
+    `}},yy="return a * b;";function $3(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=T.upcastType(n.dtype,r.dtype);if(n.dtype==="complex64"){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),u=new gy(my.REAL,n.shape,r.shape),p=new gy(my.IMAG,n.shape,r.shape),c=[{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:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=us({inputs:{real:d,imag:h},backend:a});return a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}if(a.shouldExecuteOnCPU([n,r])){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),[u,p]=SU(n.shape,r.shape,o.values,l.values,s),c=a.makeTensorInfo(p,s),d=a.texData.get(c.dataId);return d.values=u,c}let i;return V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new fp(yy,n.shape,r.shape):i=new yl(yy,n.shape,r.shape),a.runWebGLProgram(i,[n,r],s)}var NG={kernelName:Li,backendName:"webgl",kernelFunc:$3};function EG(e,t,a){let n=[Hs(e.shape),...js(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Hs(t),...js(t)],i=new V6(s,n),o=!0,l=[n],u=a.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{shape:s}=n,i=a,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!gd(r.shape,l)&&!(p.texture!==null&&gd(p.shape,l))?EG(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var RG={kernelName:Hl,backendName:"webgl",kernelFunc:pe},xy=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=`
         if (inIdx < 0 || inIdx >= ${r}) {
           return 0.0;
         }
@@ -1494,12 +1494,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function $G(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let a=t.length?t[t.length-1].outSize:e[1],n=T.computeOptimalWindowSize(a);t.push({inSize:a,windowSize:n,outSize:Math.ceil(a/n)})}return t}function vo(e,t,a,n){let r=$G(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],p,c;a==="mean"?p=i===0?new Ay({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Ay({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):p=new MG({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},a),c=s,s=n.runWebGLProgram(p,[s],t),c.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(c)}return s}var _G=class{constructor(e,t){this.variableNames=["A"];let a=new Array(e.length);for(let s=0;s<a.length;s++)a[s]=e[t[s]];this.outputShape=a,this.rank=a.length;let n=gt(this.rank),r=PG(t);this.userCode=`
+    `}};function $G(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let a=t.length?t[t.length-1].outSize:e[1],n=T.computeOptimalWindowSize(a);t.push({inSize:a,windowSize:n,outSize:Math.ceil(a/n)})}return t}function vo(e,t,a,n){let r=$G(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],p,c;a==="mean"?p=i===0?new xy({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new xy({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):p=new MG({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},a),c=s,s=n.runWebGLProgram(p,[s],t),c.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(c)}return s}var _G=class{constructor(e,t){this.variableNames=["A"];let a=new Array(e.length);for(let s=0;s<a.length;s++)a[s]=e[t[s]];this.outputShape=a,this.rank=a.length;let n=gt(this.rank),r=PG(t);this.userCode=`
     void main() {
       ${n} resRC = getOutputCoords();
       setOutput(getA(${r}));
     }
-    `}};function PG(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r<e.length;r++)n[e[r]]=a[r];return n.join()}var FG=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let a=new Array(e.length);for(let u=0;u<a.length;u++)a[u]=e[t[u]];if(this.outputShape=a,this.rank=a.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=gt(this.rank),r=V6("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${a[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function PG(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r<e.length;r++)n[e[r]]=a[r];return n.join()}var FG=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let a=new Array(e.length);for(let u=0;u<a.length;u++)a[u]=e[t[u]];if(this.outputShape=a,this.rank=a.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=gt(this.rank),r=W6("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${a[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${n} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1516,14 +1516,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function $h(e,t,a){let n=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new FG(e.shape,t):new _G(e.shape,t);return a.runWebGLProgram(n,[e],e.dtype)}function OG(e,t,a,n){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=T.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=$h(e,l,n),o=T.getInnerMostAxes(o.length,s)),T.assertAxesAreInnerMostDims("sum",o,s);let[c,d]=T.computeOutAndReduceShapes(p.shape,o),h=c;a&&(h=T.expandShapeToKeepDim(c,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,g=pe({inputs:{x:p},attrs:{shape:[m,f]},backend:n}),y=Kd(e.dtype),x=vo(g,y,"sum",n),A=pe({inputs:{x},attrs:{shape:h},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x),u&&n.disposeIntermediateTensorInfo(p),A}function _h(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return OG(r,s,i,a)}var DG={kernelName:io,backendName:"webgl",kernelFunc:_h};function Sa(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{perm:s}=n,i=a,o=r.shape.length,l=new Array(o);for(let p=0;p<l.length;p++)l[p]=r.shape[s[p]];let u;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,c=R3(p,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(u.dataId);d.values=c}else u=$h(r,s,i);return u}var zG={kernelName:Ar,backendName:"webgl",kernelFunc:Sa},Z6=1e3;function Bc({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[p-2]:t.shape[p-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),A=xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],k=n?[x,f,d]:[x,d,f],S=pe({inputs:{x:e},backend:r,attrs:{shape:b}}),C=pe({inputs:{x:t},backend:r,attrs:{shape:k}}),N=[S,C],$=Math.max(y,x),M=a?S.shape[1]:S.shape[2],R=s!=null,I=i!=null,_=l==="leakyrelu",D=l!=null?xd(l,!0):null,W=R||I||_||D!=null,P;if((h===1||f===1)&&M>Z6&&W===!1){let G=S,q=C;a&&(G=Sa({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),N.push(G)),n&&(q=Sa({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),N.push(q));let H=f!==1,B=f===1,Z=G;H&&(Z=pe({inputs:{x:G},backend:r,attrs:{shape:[$,M,1]}}),N.push(Z));let X=f===1?2:1,ne=q;B&&(ne=pe({inputs:{x:q},backend:r,attrs:{shape:[$,1,M]}}),N.push(ne));let ee=$3({inputs:{a:Z,b:ne},backend:r});P=_h({inputs:{x:ee},backend:r,attrs:{axis:X,keepDims:!0}}),N.push(ee)}else{let G=fa(e.dtype,t.dtype),q=new K6(b,k,[$,h,f],a,n,R,D,I,_),H=[S,C];if(s!=null&&H.push(s),I&&H.push(i),_){let B=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(B),N.push(B)}P=r.runWebGLProgram(q,H,G)}let U=pe({inputs:{x:P},backend:r,attrs:{shape:A}});N.push(P);for(let G of N)r.disposeIntermediateTensorInfo(G);return U}function LG(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Bc({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var BG={kernelName:jr,backendName:"webgl",kernelFunc:LG},by="return abs(x);";function WG(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=B6(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return V().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Vr(n.shape,by):r=new qn(n.shape,by),a.runWebGLProgram(r,[n],n.dtype)}var VG={kernelName:vl,backendName:"webgl",kernelFunc:WG},UG=Nn+`
+    `}};function Mh(e,t,a){let n=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new FG(e.shape,t):new _G(e.shape,t);return a.runWebGLProgram(n,[e],e.dtype)}function OG(e,t,a,n){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=T.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Mh(e,l,n),o=T.getInnerMostAxes(o.length,s)),T.assertAxesAreInnerMostDims("sum",o,s);let[c,d]=T.computeOutAndReduceShapes(p.shape,o),h=c;a&&(h=T.expandShapeToKeepDim(c,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,g=pe({inputs:{x:p},attrs:{shape:[m,f]},backend:n}),y=Xd(e.dtype),x=vo(g,y,"sum",n),A=pe({inputs:{x},attrs:{shape:h},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x),u&&n.disposeIntermediateTensorInfo(p),A}function $h(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return OG(r,s,i,a)}var DG={kernelName:io,backendName:"webgl",kernelFunc:$h};function Sa(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{perm:s}=n,i=a,o=r.shape.length,l=new Array(o);for(let p=0;p<l.length;p++)l[p]=r.shape[s[p]];let u;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,c=R3(p,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(u.dataId);d.values=c}else u=Mh(r,s,i);return u}var zG={kernelName:Ar,backendName:"webgl",kernelFunc:Sa},K6=1e3;function Lc({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[p-2]:t.shape[p-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),A=xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],k=n?[x,f,d]:[x,d,f],S=pe({inputs:{x:e},backend:r,attrs:{shape:b}}),C=pe({inputs:{x:t},backend:r,attrs:{shape:k}}),N=[S,C],$=Math.max(y,x),M=a?S.shape[1]:S.shape[2],R=s!=null,I=i!=null,_=l==="leakyrelu",D=l!=null?yd(l,!0):null,W=R||I||_||D!=null,P;if((h===1||f===1)&&M>K6&&W===!1){let G=S,q=C;a&&(G=Sa({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),N.push(G)),n&&(q=Sa({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),N.push(q));let H=f!==1,B=f===1,Z=G;H&&(Z=pe({inputs:{x:G},backend:r,attrs:{shape:[$,M,1]}}),N.push(Z));let X=f===1?2:1,ne=q;B&&(ne=pe({inputs:{x:q},backend:r,attrs:{shape:[$,1,M]}}),N.push(ne));let ee=$3({inputs:{a:Z,b:ne},backend:r});P=$h({inputs:{x:ee},backend:r,attrs:{axis:X,keepDims:!0}}),N.push(ee)}else{let G=fa(e.dtype,t.dtype),q=new X6(b,k,[$,h,f],a,n,R,D,I,_),H=[S,C];if(s!=null&&H.push(s),I&&H.push(i),_){let B=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(B),N.push(B)}P=r.runWebGLProgram(q,H,G)}let U=pe({inputs:{x:P},backend:r,attrs:{shape:A}});N.push(P);for(let G of N)r.disposeIntermediateTensorInfo(G);return U}function LG(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Lc({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var BG={kernelName:jr,backendName:"webgl",kernelFunc:LG},Ay="return abs(x);";function WG(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=L6(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return V().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Vr(n.shape,Ay):r=new qn(n.shape,Ay),a.runWebGLProgram(r,[n],n.dtype)}var VG={kernelName:vl,backendName:"webgl",kernelFunc:WG},UG=Nn+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
 `,GG=Qe({opSnippet:UG}),HG={kernelName:kl,backendName:"webgl",kernelFunc:GG},jG=Nn+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,qG=Qe({opSnippet:jG}),XG={kernelName:wl,backendName:"webgl",kernelFunc:qG},vy="return a + b;",KG=da({opSnippet:vy,packedOpSnippet:vy,supportsComplex:!0,cpuKernelImpl:sU}),ZG={kernelName:as,backendName:"webgl",kernelFunc:KG},YG=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,qG=Qe({opSnippet:jG}),XG={kernelName:wl,backendName:"webgl",kernelFunc:qG},by="return a + b;",KG=da({opSnippet:by,packedOpSnippet:by,supportsComplex:!0,cpuKernelImpl:sU}),ZG={kernelName:as,backendName:"webgl",kernelFunc:KG},YG=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${a.join(`
         `)}
@@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,qG=Qe({opSnippet:jG}),XG={kernelName:wl,back
         vec4 result = ${n};
         setOutput(result);
       }
-    `}};function Ic(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return Ya({inputs:{x:n[0]},backend:a});if(n.length>V().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=Ic({inputs:n.slice(0,o),backend:a}),u=Ic({inputs:n.slice(o),backend:a});return Ic({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>fa(o,l)),s=n.map(o=>o.shape),i=V().getBool("WEBGL_PACK")?new JG(n[0].shape,s):new YG(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var QG={kernelName:Ks,backendName:"webgl",kernelFunc:Ic};function eH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Sa({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"all",a),y;if(i){let x=T.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var tH={kernelName:Zs,backendName:"webgl",kernelFunc:eH};function aH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Sa({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"any",a),y;if(i){let x=T.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var nH={kernelName:Ys,backendName:"webgl",kernelFunc:aH},rH=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function wc(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return Ya({inputs:{x:n[0]},backend:a});if(n.length>V().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=wc({inputs:n.slice(0,o),backend:a}),u=wc({inputs:n.slice(o),backend:a});return wc({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>fa(o,l)),s=n.map(o=>o.shape),i=V().getBool("WEBGL_PACK")?new JG(n[0].shape,s):new YG(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var QG={kernelName:Ks,backendName:"webgl",kernelFunc:wc};function eH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Sa({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"all",a),y;if(i){let x=T.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var tH={kernelName:Zs,backendName:"webgl",kernelFunc:eH};function aH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Sa({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"any",a),y;if(i){let x=T.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var nH={kernelName:Ys,backendName:"webgl",kernelFunc:aH},rH=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1621,7 +1621,7 @@ return log(x + sqrt(x * x - 1.0));`,qG=Qe({opSnippet:jG}),XG={kernelName:wl,back
         }
         setOutput(bestIndex);
       }
-    `}};function Y6(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new rH(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=Y6(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function J6(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new sH(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=J6(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function Q6(e,t,a,n){let r=[a];if(T.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!V().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,p]=T.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=Y6(e,d,n);s.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return J6(e,t,n)}function iH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sa({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=Q6(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var oH={kernelName:Js,backendName:"webgl",kernelFunc:iH};function lH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sa({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=Q6(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var uH={kernelName:Sd,backendName:"webgl",kernelFunc:lH},dH=Nn+`
+    `}};function Z6(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new rH(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=Z6(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function Y6(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new sH(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=Y6(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function J6(e,t,a,n){let r=[a];if(T.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!V().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,p]=T.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=Z6(e,d,n);s.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return Y6(e,t,n)}function iH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sa({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=J6(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var oH={kernelName:Js,backendName:"webgl",kernelFunc:iH};function lH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sa({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=J6(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var uH={kernelName:Id,backendName:"webgl",kernelFunc:lH},dH=Nn+`
   if (abs(x) > 1.) {
     return NAN;
   }
@@ -1635,11 +1635,11 @@ return log(x + sqrt(x * x - 1.0));`,qG=Qe({opSnippet:jG}),XG={kernelName:wl,back
   bvec4 isNaNA = isnan(a);
   bvec4 isNaNB = isnan(b);
   bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
-  `+fp+`
+  `+hp+`
   return result;
 `,vH=da({opSnippet:AH,packedOpSnippet:bH}),kH={kernelName:Nl,backendName:"webgl",kernelFunc:vH},wH=Nn+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelName:Cl,backendName:"webgl",kernelFunc:IH},Ad=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)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,p=e.effectiveFilterHeight,c=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`,y="0.0";if(f||(y="-1.0 / 1e-20"),a){let C=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelName:Cl,backendName:"webgl",kernelFunc:IH},xd=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)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,p=e.effectiveFilterHeight,c=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) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),a){let C=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${h});
 
@@ -1943,7 +1943,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         }
         setOutput(${k});
       }
-    `}};function TH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;uu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Ya({inputs:{x:r},backend:a});let c=new Ad(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var CH={kernelName:Qs,backendName:"webgl",kernelFunc:TH};function NH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new _3(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var EH={kernelName:Zc,backendName:"webgl",kernelFunc:NH},RH=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=`
+    `}};function TH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;uu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Ya({inputs:{x:r},backend:a});let c=new xd(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var CH={kernelName:Qs,backendName:"webgl",kernelFunc:TH};function NH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new _3(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var EH={kernelName:Kc,backendName:"webgl",kernelFunc:NH},RH=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=`
       const ivec2 pads = ivec2(${u}, ${p});
       const float avgMultiplier = float(${c});
 
@@ -2041,7 +2041,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function $H(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=T.computePool3DInfo(i.shape,o,l,c,u,p),h=new MH(d);return a.runWebGLProgram(h,[r],i.dtype)}var _H={kernelName:L2,backendName:"webgl",kernelFunc:$H};function PH(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;uu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=new RH(p);return a.runWebGLProgram(c,[r],i.dtype)}var FH={kernelName:Kc,backendName:"webgl",kernelFunc:PH};function OH(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Bc({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var DH={kernelName:ei,backendName:"webgl",kernelFunc:OH},zH=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function $H(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=T.computePool3DInfo(i.shape,o,l,c,u,p),h=new MH(d);return a.runWebGLProgram(h,[r],i.dtype)}var _H={kernelName:L2,backendName:"webgl",kernelFunc:$H};function PH(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;uu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=new RH(p);return a.runWebGLProgram(c,[r],i.dtype)}var FH={kernelName:Xc,backendName:"webgl",kernelFunc:PH};function OH(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Lc({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var DH={kernelName:ei,backendName:"webgl",kernelFunc:OH},zH=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -2103,7 +2103,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         ${o}
         setOutput(result);
       }
-    `}};function HH(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=St.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function gu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=St.parseSliceParams(r,s,i);if(St.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=OU(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=St.isSliceContinous(r.shape,o,l);if(u||!p){let c=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GH(l):new VH(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),HH(r,o,l,a)}var jH={kernelName:Xl,backendName:"webgl",kernelFunc:gu},qH=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=[],f=pe({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Sa({inputs:{x:f},backend:a,attrs:{perm:u}}),g=pe({inputs:{x:m},backend:a,attrs:{shape:p}}),y=gu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>a.disposeIntermediateTensorInfo(x)),y},XH={kernelName:El,backendName:"webgl",kernelFunc:qH};function KH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=L6(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var ZH={kernelName:Td,backendName:"webgl",kernelFunc:KH};function YH(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var JH={kernelName:Yc,backendName:"webgl",kernelFunc:YH},QH="return float(a != b);",ev=da({opSnippet:QH,cpuKernelImpl:CU,dtype:"bool"}),ej={kernelName:Bi,backendName:"webgl",kernelFunc:ev};function gp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Ya({inputs:{x:r.complexTensorInfos.real},backend:a})}var tj={kernelName:zd,backendName:"webgl",kernelFunc:gp},aj="return float(int(x));";function nj(e,t){let a=new qn(e.shape,aj),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function w2(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Ya({inputs:{x:r},backend:a});let i=mn(r.shape),o=w2({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=us({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=gp({inputs:{input:r},backend:a}),o=w2({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Ya({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=oU(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return nj(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=ev({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var rj={kernelName:ti,backendName:"webgl",kernelFunc:w2},ky="return ceil(x);",sj=Qe({opSnippet:ky,packedOpSnippet:ky,cpuKernelImpl:lU}),ij={kernelName:ai,backendName:"webgl",kernelFunc:sj},oj=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}};function HH(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=St.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function gu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=St.parseSliceParams(r,s,i);if(St.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=OU(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=St.isSliceContinous(r.shape,o,l);if(u||!p){let c=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GH(l):new VH(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),HH(r,o,l,a)}var jH={kernelName:Xl,backendName:"webgl",kernelFunc:gu},qH=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=[],f=pe({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Sa({inputs:{x:f},backend:a,attrs:{perm:u}}),g=pe({inputs:{x:m},backend:a,attrs:{shape:p}}),y=gu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>a.disposeIntermediateTensorInfo(x)),y},XH={kernelName:El,backendName:"webgl",kernelFunc:qH};function KH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=z6(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var ZH={kernelName:Sd,backendName:"webgl",kernelFunc:KH};function YH(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var JH={kernelName:Zc,backendName:"webgl",kernelFunc:YH},QH="return float(a != b);",Q6=da({opSnippet:QH,cpuKernelImpl:CU,dtype:"bool"}),ej={kernelName:Bi,backendName:"webgl",kernelFunc:Q6};function mp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Ya({inputs:{x:r.complexTensorInfos.real},backend:a})}var tj={kernelName:Dd,backendName:"webgl",kernelFunc:mp},aj="return float(int(x));";function nj(e,t){let a=new qn(e.shape,aj),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function w2(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Ya({inputs:{x:r},backend:a});let i=mn(r.shape),o=w2({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=us({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=mp({inputs:{input:r},backend:a}),o=w2({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Ya({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=oU(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return nj(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=Q6({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var rj={kernelName:ti,backendName:"webgl",kernelFunc:w2},vy="return ceil(x);",sj=Qe({opSnippet:vy,packedOpSnippet:vy,cpuKernelImpl:lU}),ij={kernelName:ai,backendName:"webgl",kernelFunc:sj},oj=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();
@@ -2138,7 +2138,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function wy(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function cj(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.texData.get(n.dataId),s=new pj(n.shape),i=[wy(n,r.complexTensorInfos.real),wy(n,r.complexTensorInfos.imag)];return a.runWebGLProgram(s,i,i[0].dtype)}var hj={kernelName:Jc,backendName:"webgl",kernelFunc:cj},fj=class{constructor(e){this.outputShape=[],this.outputShape=T.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 a=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];a.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,r=t[t.length-1];a.push(`else setOutput(getT${n}(yR, yC-${r}));`),this.userCode=`
+    `}};function ky(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function cj(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.texData.get(n.dataId),s=new pj(n.shape),i=[ky(n,r.complexTensorInfos.real),ky(n,r.complexTensorInfos.imag)];return a.runWebGLProgram(s,i,i[0].dtype)}var hj={kernelName:Yc,backendName:"webgl",kernelFunc:cj},fj=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${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 a=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];a.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,r=t[t.length-1];a.push(`else setOutput(getT${n}(yR, yC-${r}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -2153,12 +2153,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];c+=`
         if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
           return getChannel(
-            getT${f}(${mc(i,l,m)}),
-            vec2(${mc(u,l,m)}));
+            getT${f}(${fc(i,l,m)}),
+            vec2(${fc(u,l,m)}));
         }`}let d=o.length,h=o[o.length-1];c+=`
         return getChannel(
-          getT${d}(${mc(i,l,h)}),
-          vec2(${mc(u,l,h)}));`,this.userCode=`
+          getT${d}(${fc(i,l,h)}),
+          vec2(${fc(u,l,h)}));`,this.userCode=`
       float getValue(${i.map(f=>"int "+f)}) {
         ${c}
       }
@@ -2184,7 +2184,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         }
         setOutput(result);
       }
-    `}};function mc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function Ph(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Ya({inputs:{x:r.complexTensorInfos.imag},backend:a})}var gj={kernelName:Fd,backendName:"webgl",kernelFunc:Ph};function Zu(e,t,a){let n=e[0].dtype;if(n==="complex64"){let h=e.map(x=>gp({inputs:{input:x},backend:a})),f=e.map(x=>Ph({inputs:{input:x},backend:a})),m=Zu(h,t,a),g=Zu(f,t,a),y=us({inputs:{real:m,imag:g},backend:a});return h.forEach(x=>a.disposeIntermediateTensorInfo(x)),f.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let h=e.map(b=>{let k=[-1,v.sizeFromShape(b.shape.slice(t))];return pe({inputs:{x:b},backend:a,attrs:{shape:k}})}),f=h.map(b=>({vals:a.readSync(b.dataId),shape:b.shape})),m=T.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=uU(f,m,n,g),x=T.computeOutShape(e.map(b=>b.shape),t),A=a.makeTensorInfo(x,n,y);return h.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new qn(e[0].shape,Dr):new Vr(e[0].shape,Dr);return a.runWebGLProgram(h,e,n)}let o=V().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let m=0;m<s.length;m+=o){let g=s.slice(m,m+o);h.push(Zu(g,t,a))}let f=Zu(h,t,a);for(let m of h)a.disposeIntermediateTensorInfo(m);return f}if(i){let h=new mj(s.map(f=>f.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=yj(s,t,a),p=new fj(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=pe({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function yj(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>pe({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function tv(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?Ya({inputs:{x:l[0]},backend:a}):Zu(l,s,a)}var xj={kernelName:Rl,backendName:"webgl",kernelFunc:tv},av=class{constructor(e,t=!1,a=null,n=!1,r=!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,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,x=m?3:1,A="",b="";a&&(n?A=`float activation(float a) {
+    `}};function fc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function _h(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Ya({inputs:{x:r.complexTensorInfos.imag},backend:a})}var gj={kernelName:Pd,backendName:"webgl",kernelFunc:_h};function Ku(e,t,a){let n=e[0].dtype;if(n==="complex64"){let h=e.map(x=>mp({inputs:{input:x},backend:a})),f=e.map(x=>_h({inputs:{input:x},backend:a})),m=Ku(h,t,a),g=Ku(f,t,a),y=us({inputs:{real:m,imag:g},backend:a});return h.forEach(x=>a.disposeIntermediateTensorInfo(x)),f.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let h=e.map(b=>{let k=[-1,v.sizeFromShape(b.shape.slice(t))];return pe({inputs:{x:b},backend:a,attrs:{shape:k}})}),f=h.map(b=>({vals:a.readSync(b.dataId),shape:b.shape})),m=T.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=uU(f,m,n,g),x=T.computeOutShape(e.map(b=>b.shape),t),A=a.makeTensorInfo(x,n,y);return h.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new qn(e[0].shape,Dr):new Vr(e[0].shape,Dr);return a.runWebGLProgram(h,e,n)}let o=V().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let m=0;m<s.length;m+=o){let g=s.slice(m,m+o);h.push(Ku(g,t,a))}let f=Ku(h,t,a);for(let m of h)a.disposeIntermediateTensorInfo(m);return f}if(i){let h=new mj(s.map(f=>f.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=yj(s,t,a),p=new fj(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=pe({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function yj(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>pe({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function ev(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?Ya({inputs:{x:l[0]},backend:a}):Ku(l,s,a)}var xj={kernelName:Rl,backendName:"webgl",kernelFunc:ev},tv=class{constructor(e,t=!1,a=null,n=!1,r=!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,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,x=m?3:1,A="",b="";a&&(n?A=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${a}
         }`:r?A=`float activation(float a) {
@@ -2406,7 +2406,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},nv=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ea(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=`
+    `}},av=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ea(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=`
        int xR; int xC; int xCOffset;
        vec4 wTexel; vec4 previous; vec4 final;`;for(let m=0;m<u;m++)c+=`
            vec4 xTexelC${m*2};
@@ -2649,7 +2649,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
 
         ${n.output} = result;
       }
-    `}};function Wc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function rv({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(s!=null){let x=Wc(s.shape,h);x!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:x}}),y.push(s))}if(r!=null){let x=Wc(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:x}}),y.push(r))}if(!((c===1||d===1)&&p>Z6)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let x=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,x,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(yd(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let k=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(k);let S=Bc({a:A,b:k,backend:n,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=n.texData.get(S.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,C.shape=a.outShape,g=Ya({inputs:{x:S},backend:n}),g.shape=a.outShape,y.push(S)}else{let x=a.outHeight*a.outWidth,A=pe({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,x,a.inChannels]:[a.batchSize,a.inChannels,x]}}),b=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),k=Bc({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:m,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=pe({inputs:{x:k},backend:n,attrs:{shape:a.outShape}}),y.push(A),y.push(b),y.push(k)}for(let x of y)n.disposeIntermediateTensorInfo(x);return g}function sv({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,f=h==="channelsLast",m=l*u*p,g=d*c,y=[a.batchSize,m,g],x=!0,A=!1,b=[];if(s!=null){let G=Wc(s.shape,f);G!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:G}}),b.push(s))}if(r!=null){let G=Wc(r.shape,f);G!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:G}}),b.push(r))}let k=pe({inputs:{x:t},backend:n,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(k);let S=new bj(y,a),C=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(S,[e],"float32",C),$=pe({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push($);let M=r!=null,R=s!=null,I=o==="leakyrelu",_=o?xd(o,!0):null,D=new K6(f?$.shape:k.shape,f?k.shape:$.shape,f?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],x,A,M,_,R,I),W=f?[$,k]:[k,$];if(r&&W.push(r),R&&W.push(s),I){let G=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));W.push(G),b.push(G)}let P=n.runWebGLProgram(D,W,"float32"),U=pe({inputs:{x:P},backend:n,attrs:{shape:a.outShape}});b.push(P);for(let G of b)n.disposeIntermediateTensorInfo(G);return U}function vj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),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=rv({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&V().getBool("WEBGL_EXP_CONV")){let m=new nv(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(m,[r,s],"float32",g)}else if(V().getBool("WEBGL_CONV_IM2COL"))h=sv({x:r,filter:s,convInfo:d,backend:a});else{let m=new av(d);h=a.runWebGLProgram(m,[r,s],"float32")}let f=pe({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),f}var kj={kernelName:ni,backendName:"webgl",kernelFunc:vj},wj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function Bc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function nv({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(s!=null){let x=Bc(s.shape,h);x!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:x}}),y.push(s))}if(r!=null){let x=Bc(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:x}}),y.push(r))}if(!((c===1||d===1)&&p>K6)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let x=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,x,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(gd(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let k=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(k);let S=Lc({a:A,b:k,backend:n,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=n.texData.get(S.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,C.shape=a.outShape,g=Ya({inputs:{x:S},backend:n}),g.shape=a.outShape,y.push(S)}else{let x=a.outHeight*a.outWidth,A=pe({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,x,a.inChannels]:[a.batchSize,a.inChannels,x]}}),b=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),k=Lc({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:m,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=pe({inputs:{x:k},backend:n,attrs:{shape:a.outShape}}),y.push(A),y.push(b),y.push(k)}for(let x of y)n.disposeIntermediateTensorInfo(x);return g}function rv({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,f=h==="channelsLast",m=l*u*p,g=d*c,y=[a.batchSize,m,g],x=!0,A=!1,b=[];if(s!=null){let G=Bc(s.shape,f);G!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:G}}),b.push(s))}if(r!=null){let G=Bc(r.shape,f);G!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:G}}),b.push(r))}let k=pe({inputs:{x:t},backend:n,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(k);let S=new bj(y,a),C=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(S,[e],"float32",C),$=pe({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push($);let M=r!=null,R=s!=null,I=o==="leakyrelu",_=o?yd(o,!0):null,D=new X6(f?$.shape:k.shape,f?k.shape:$.shape,f?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],x,A,M,_,R,I),W=f?[$,k]:[k,$];if(r&&W.push(r),R&&W.push(s),I){let G=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));W.push(G),b.push(G)}let P=n.runWebGLProgram(D,W,"float32"),U=pe({inputs:{x:P},backend:n,attrs:{shape:a.outShape}});b.push(P);for(let G of b)n.disposeIntermediateTensorInfo(G);return U}function vj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),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=nv({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&V().getBool("WEBGL_EXP_CONV")){let m=new av(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(m,[r,s],"float32",g)}else if(V().getBool("WEBGL_CONV_IM2COL"))h=rv({x:r,filter:s,convInfo:d,backend:a});else{let m=new tv(d);h=a.runWebGLProgram(m,[r,s],"float32")}let f=pe({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),f}var kj={kernelName:ni,backendName:"webgl",kernelFunc:vj},wj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2843,7 +2843,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function Cj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new wj(d);return a.runWebGLProgram(h,[r,s],"float32")}var Nj={kernelName:Nd,backendName:"webgl",kernelFunc:Cj};function Ej(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=new Ij(d);return a.runWebGLProgram(h,[r,s],"float32")}var Rj={kernelName:ri,backendName:"webgl",kernelFunc:Ej};function Mj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new Aj(u);return a.runWebGLProgram(p,[r,s],"float32")}var $j={kernelName:Qc,backendName:"webgl",kernelFunc:Mj};function _j(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new Sj(u);return a.runWebGLProgram(p,[r,s],"float32")}var Pj={kernelName:B2,backendName:"webgl",kernelFunc:_j};function Fj(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Tj(u);return a.runWebGLProgram(p,[r,s],"float32")}var Oj={kernelName:eh,backendName:"webgl",kernelFunc:Fj},Dj=mu+`
+    `}};function Cj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new wj(d);return a.runWebGLProgram(h,[r,s],"float32")}var Nj={kernelName:Cd,backendName:"webgl",kernelFunc:Cj};function Ej(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=new Ij(d);return a.runWebGLProgram(h,[r,s],"float32")}var Rj={kernelName:ri,backendName:"webgl",kernelFunc:Ej};function Mj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new Aj(u);return a.runWebGLProgram(p,[r,s],"float32")}var $j={kernelName:Jc,backendName:"webgl",kernelFunc:Mj};function _j(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new Sj(u);return a.runWebGLProgram(p,[r,s],"float32")}var Pj={kernelName:B2,backendName:"webgl",kernelFunc:_j};function Fj(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Tj(u);return a.runWebGLProgram(p,[r,s],"float32")}var Oj={kernelName:Qc,backendName:"webgl",kernelFunc:Fj},Dj=mu+`
   return cos(x);
 `,zj=Qe({opSnippet:Dj}),Lj={kernelName:si,backendName:"webgl",kernelFunc:zj},Bj=`
   float e2x = exp(-x);
@@ -2909,20 +2909,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
           setOutput(newValue);
         }
       }
-    `}},Gj=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Uj(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},Hj={kernelName:ui,backendName:"webgl",kernelFunc:Gj},bd;(function(e){e.Prod="*",e.Sum="+"})(bd||(bd={}));var Iy=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===bd.Prod?"1.0":"0.0",i=a?s:`getX(${Sy(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},Gj=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Uj(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},Hj={kernelName:ui,backendName:"webgl",kernelFunc:Gj},Ad;(function(e){e.Prod="*",e.Sum="+"})(Ad||(Ad={}));var wy=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Ad.Prod?"1.0":"0.0",i=a?s:`getX(${Iy(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${gt(r)} coords = getOutputCoords();
-        int end = ${Ty(r,"coords",this.op)};
+        int end = ${Sy(r,"coords",this.op)};
         float val = ${i};
         int pow2 = int(pow(2.0, index));
         if (${l}) {
           int idx = ${u};
-          ${Ty(r,"coords",this.op)} = idx;
-          val ${this.op}= getX(${Sy(r,"coords",this.op)});
+          ${Sy(r,"coords",this.op)} = idx;
+          val ${this.op}= getX(${Iy(r,"coords",this.op)});
         }
         setOutput(val);
       }
-    `}};function Sy(e,t,a){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 new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Ty(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function iv(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=Sa({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Ya({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new Iy(e,l.shape,!1,s),f=[[d]],m=c;c=a.runWebGLProgram(h,[c],c.dtype,f),a.disposeIntermediateTensorInfo(m)}if(r){let d=new Iy(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=Sa({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function jj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return iv(bd.Prod,r,a,s,i,o)}var qj={kernelName:oi,backendName:"webgl",kernelFunc:jj};function Xj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return iv(bd.Sum,r,a,s,i,o)}var Kj={kernelName:li,backendName:"webgl",kernelFunc:Xj};function Zj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=L6(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=iU(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Yj={kernelName:Ed,backendName:"webgl",kernelFunc:Zj},Jj=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=`
+    `}};function Iy(e,t,a){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 new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Sy(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function sv(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=Sa({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Ya({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new wy(e,l.shape,!1,s),f=[[d]],m=c;c=a.runWebGLProgram(h,[c],c.dtype,f),a.disposeIntermediateTensorInfo(m)}if(r){let d=new wy(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=Sa({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function jj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return sv(Ad.Prod,r,a,s,i,o)}var qj={kernelName:oi,backendName:"webgl",kernelFunc:jj};function Xj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return sv(Ad.Sum,r,a,s,i,o)}var Kj={kernelName:li,backendName:"webgl",kernelFunc:Xj};function Zj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=z6(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=iU(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Yj={kernelName:Nd,backendName:"webgl",kernelFunc:Zj},Jj=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2941,7 +2941,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={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 Qj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=new Jj(f,s,i);return a.runWebGLProgram(m,[r],r.dtype)}var eq={kernelName:di,backendName:"webgl",kernelFunc:Qj},ov=class{constructor(e,t=!1,a=null,n=!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=Ea(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(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 Qj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=new Jj(f,s,i);return a.runWebGLProgram(m,[r],r.dtype)}var eq={kernelName:di,backendName:"webgl",kernelFunc:Qj},iv=class{constructor(e,t=!1,a=null,n=!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=Ea(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${a}
         }`:r?l=`float activation(float a) {
@@ -2994,7 +2994,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         ${u}
         setOutput(result);
       }
-    `}},lv=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ea(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=`
+    `}},ov=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ea(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<p;g++)d+=`
           vec4 xTexelC${g*2};
@@ -3183,7 +3183,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         ${f}
         setOutput(result);
       }
-    `}};function tq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,p=l;p==null&&(p=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;V().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new lv(c):d=new ov(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var aq={kernelName:pi,backendName:"webgl",kernelFunc:tq},nq=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function tq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,p=l;p==null&&(p=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;V().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new ov(c):d=new iv(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var aq={kernelName:pi,backendName:"webgl",kernelFunc:tq},nq=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -3263,13 +3263,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function sq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new nq(c);return a.runWebGLProgram(d,[r,s],"float32")}var iq={kernelName:th,backendName:"webgl",kernelFunc:sq};function oq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new rq(c);return a.runWebGLProgram(d,[r,s],"float32")}var lq={kernelName:ah,backendName:"webgl",kernelFunc:oq},uq=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function sq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new nq(c);return a.runWebGLProgram(d,[r,s],"float32")}var iq={kernelName:eh,backendName:"webgl",kernelFunc:sq};function oq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new rq(c);return a.runWebGLProgram(d,[r,s],"float32")}var lq={kernelName:th,backendName:"webgl",kernelFunc:oq},uq=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 dq(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=pe({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new uq(s),l=a.runWebGLProgram(o,[i],i.dtype),u=pe({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var pq={kernelName:Rd,backendName:"webgl",kernelFunc:dq},cq=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=`
+    `}};function dq(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=pe({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new uq(s),l=a.runWebGLProgram(o,[i],i.dtype),u=pe({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var pq={kernelName:Ed,backendName:"webgl",kernelFunc:dq},cq=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=`
       const ivec2 strides = ivec2(${r}, ${s});
       const ivec2 pads = ivec2(${p}, ${c});
       const float neg_infinity = -3.4e38;
@@ -3307,7 +3307,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function hq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new cq(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=pe({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var fq={kernelName:Md,backendName:"webgl",kernelFunc:hq};function mq(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m<c;++m){for(let g of p[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=s[g]:(A=Sa({inputs:{x:s[g]},backend:a,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let k=0;k<x.length;++k)b.splice(x[k],0,1);v.arraysEqual(A.shape,b)||(A=pe({inputs:{x:A},backend:a,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=$3({inputs:{a:A,b:d},backend:a}),f.push(d))}m<c-1&&(u[m]>=0&&(d=_h({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeIntermediateTensorInfo(m);return d}var gq={kernelName:$d,backendName:"webgl",kernelFunc:mq},yq="return (x >= 0.0) ? x : (exp(x) - 1.0);",xq=`
+    `}};function hq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new cq(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=pe({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var fq={kernelName:Rd,backendName:"webgl",kernelFunc:hq};function mq(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m<c;++m){for(let g of p[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=s[g]:(A=Sa({inputs:{x:s[g]},backend:a,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let k=0;k<x.length;++k)b.splice(x[k],0,1);v.arraysEqual(A.shape,b)||(A=pe({inputs:{x:A},backend:a,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=$3({inputs:{a:A,b:d},backend:a}),f.push(d))}m<c-1&&(u[m]>=0&&(d=$h({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeIntermediateTensorInfo(m);return d}var gq={kernelName:Md,backendName:"webgl",kernelFunc:mq},yq="return (x >= 0.0) ? x : (exp(x) - 1.0);",xq=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3319,7 +3319,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
 `,Aq=Qe({opSnippet:yq,packedOpSnippet:xq}),bq={kernelName:hi,backendName:"webgl",kernelFunc:Aq},vq="return (b >= 1.0) ? a : a * (b + 1.0);",kq=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,wq=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new mp(kq,n.shape,r.shape):new yl(vq,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},Iq={kernelName:W2,backendName:"webgl",kernelFunc:wq},Sq=`
+`,wq=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new fp(kq,n.shape,r.shape):new yl(vq,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},Iq={kernelName:W2,backendName:"webgl",kernelFunc:wq},Sq=`
   return vec4(equal(a, b));
 `,Tq="return float(a == b);",Cq=da({opSnippet:Tq,packedOpSnippet:Sq,dtype:"bool",cpuKernelImpl:dU}),Nq={kernelName:fi,backendName:"webgl",kernelFunc:Cq},Eq=`
   // Error function is calculated approximately with elementary function.
@@ -3347,7 +3347,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,uv=Qe({opSnippet:$q,packedOpSnippet:_q,cpuKernelImpl:pU,dtype:"float32"}),Pq={kernelName:mi,backendName:"webgl",kernelFunc:uv};function I2(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),pe({inputs:{x:s},backend:n,attrs:{shape:o}})}var Fq={kernelName:$l,backendName:"webgl",kernelFunc:I2},Cy="return exp(x) - 1.0;",Oq=Qe({opSnippet:Cy,packedOpSnippet:Cy,cpuKernelImpl:cU}),Dq={kernelName:_l,backendName:"webgl",kernelFunc:Oq},Ny=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${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 ${e}.`);this.userCode=`
+`,lv=Qe({opSnippet:$q,packedOpSnippet:_q,cpuKernelImpl:pU,dtype:"float32"}),Pq={kernelName:mi,backendName:"webgl",kernelFunc:lv};function I2(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),pe({inputs:{x:s},backend:n,attrs:{shape:o}})}var Fq={kernelName:$l,backendName:"webgl",kernelFunc:I2},Ty="return exp(x) - 1.0;",Oq=Qe({opSnippet:Ty,packedOpSnippet:Ty,cpuKernelImpl:cU}),Dq={kernelName:_l,backendName:"webgl",kernelFunc:Oq},Cy=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${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 = ${r};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -3380,12 +3380,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function dv(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=pe({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new Ny("real",l,t),p=new Ny("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=us({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let m=pe({inputs:{x:f},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(f),m}function zq(e){let{inputs:t,backend:a}=e,{input:n}=t;return dv(n,!1,a)}var Lq={kernelName:_d,backendName:"webgl",kernelFunc:zq},Bq=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function uv(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=pe({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new Cy("real",l,t),p=new Cy("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=us({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let m=pe({inputs:{x:f},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(f),m}function zq(e){let{inputs:t,backend:a}=e,{input:n}=t;return uv(n,!1,a)}var Lq={kernelName:$d,backendName:"webgl",kernelFunc:zq},Bq=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 yp(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new Bq(n,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Wq={kernelName:Pl,backendName:"webgl",kernelFunc:yp},Vq=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function gp(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new Bq(n,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Wq={kernelName:Pl,backendName:"webgl",kernelFunc:gp},Vq=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];
@@ -3399,7 +3399,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},Uq={kernelName:gi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Vq(a.shape);return n.runWebGLProgram(r,[a],a.dtype)}},Ey="return floor(x);",Gq=Qe({opSnippet:Ey,packedOpSnippet:Ey,cpuKernelImpl:hU}),Hq={kernelName:yi,backendName:"webgl",kernelFunc:Gq},jq=`
+    `}},Uq={kernelName:gi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Vq(a.shape);return n.runWebGLProgram(r,[a],a.dtype)}},Ny="return floor(x);",Gq=Qe({opSnippet:Ny,packedOpSnippet:Ny,cpuKernelImpl:hU}),Hq={kernelName:yi,backendName:"webgl",kernelFunc:Gq},jq=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3486,7 +3486,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
 
         ${t.output} = result;
       }
-    `}},Jq={kernelName:rd,backendName:"webgl",kernelFunc:Qq},Ko,Om=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function Qq(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],c=[u,l,s];if(o||i){let m=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ko==null||m!==Om)&&(Om=m,Ko=document.createElement("canvas").getContext("2d",{willReadFrequently:Om})),Ko.canvas.width=l,Ko.canvas.height=u,Ko.drawImage(r,0,0,l,u),r=Ko.canvas}let d=a.makeTensorInfo(p,"int32");a.texData.get(d.dataId).usage=cn.PIXELS,a.gpgpu.uploadPixelDataToTexture(a.getTexture(d.dataId),r);let h=V().getBool("WEBGL_PACK")?new Yq(c):new Zq(c),f=a.runWebGLProgram(h,[d],"int32");return a.disposeData(d.dataId),f}function eX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(p),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,m),y,x=[],A=i!=null,b=o!=null,k=h==="leakyrelu",S=()=>{let N=[r,s],$=(M,R)=>{if(R==="NCHW"&&M.shape.length===1&&M.shape[0]!==1){let I=pe({inputs:{x:M},backend:a,attrs:{shape:[M.shape[0],1,1]}});return x.push(I),I}return M};if(A&&N.push($(i,p)),b&&N.push($(o,p)),k){let M=a.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));N.push(M),x.push(M)}return N};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=rv({x:r,filter:s,convInfo:g,backend:a,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(g.strideWidth<=2&&m==="channelsLast"&&V().getBool("WEBGL_EXP_CONV")){let N=h?xd(h,!0):null,$=new nv(g,A,N,b,k),M=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=S();y=a.runWebGLProgram($,R,"float32",M)}else if(V().getBool("WEBGL_CONV_IM2COL"))y=sv({x:r,filter:s,convInfo:g,backend:a,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let N=h?xd(h,!1):null,$=new av(g,A,N,b,k),M=S();y=a.runWebGLProgram($,M,"float32")}let C=pe({inputs:{x:y},backend:a,attrs:{shape:g.outShape}});return x.push(y),x.forEach(N=>a.disposeIntermediateTensorInfo(N)),C}var tX={kernelName:qr,backendName:"webgl",kernelFunc:eX};function aX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,f=[],m=p;m==null&&(m=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=T.computeConv2DInfo(r.shape,s.shape,l,m,u,c,!0),y=V().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,x=d?xd(d,y):null,A=[r,s],b=i!=null,k=o!=null,S=d==="leakyrelu";if(b&&A.push(i),k&&A.push(o),S){let M=a.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));A.push(M),f.push(M)}let C;y?C=new lv(g,b,x,k,S):C=new ov(g,b,x,k,S);let N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=a.runWebGLProgram(C,A,"float32",N);return f.forEach(M=>a.disposeIntermediateTensorInfo(M)),$}var nX={kernelName:Xr,backendName:"webgl",kernelFunc:aX},rX=class{constructor(e,t,a,n){this.sliceDim=e,this.strides=t,this.paramsShape=n,this.variableNames=["x","indices"],this.outputShape=a;let r=gt(a.length),s=`
+    `}},Jq={kernelName:nd,backendName:"webgl",kernelFunc:Qq},Ko,Om=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function Qq(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],c=[u,l,s];if(o||i){let m=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ko==null||m!==Om)&&(Om=m,Ko=document.createElement("canvas").getContext("2d",{willReadFrequently:Om})),Ko.canvas.width=l,Ko.canvas.height=u,Ko.drawImage(r,0,0,l,u),r=Ko.canvas}let d=a.makeTensorInfo(p,"int32");a.texData.get(d.dataId).usage=cn.PIXELS,a.gpgpu.uploadPixelDataToTexture(a.getTexture(d.dataId),r);let h=V().getBool("WEBGL_PACK")?new Yq(c):new Zq(c),f=a.runWebGLProgram(h,[d],"int32");return a.disposeData(d.dataId),f}function eX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(p),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,m),y,x=[],A=i!=null,b=o!=null,k=h==="leakyrelu",S=()=>{let N=[r,s],$=(M,R)=>{if(R==="NCHW"&&M.shape.length===1&&M.shape[0]!==1){let I=pe({inputs:{x:M},backend:a,attrs:{shape:[M.shape[0],1,1]}});return x.push(I),I}return M};if(A&&N.push($(i,p)),b&&N.push($(o,p)),k){let M=a.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));N.push(M),x.push(M)}return N};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=nv({x:r,filter:s,convInfo:g,backend:a,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(g.strideWidth<=2&&m==="channelsLast"&&V().getBool("WEBGL_EXP_CONV")){let N=h?yd(h,!0):null,$=new av(g,A,N,b,k),M=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=S();y=a.runWebGLProgram($,R,"float32",M)}else if(V().getBool("WEBGL_CONV_IM2COL"))y=rv({x:r,filter:s,convInfo:g,backend:a,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let N=h?yd(h,!1):null,$=new tv(g,A,N,b,k),M=S();y=a.runWebGLProgram($,M,"float32")}let C=pe({inputs:{x:y},backend:a,attrs:{shape:g.outShape}});return x.push(y),x.forEach(N=>a.disposeIntermediateTensorInfo(N)),C}var tX={kernelName:qr,backendName:"webgl",kernelFunc:eX};function aX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,f=[],m=p;m==null&&(m=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=T.computeConv2DInfo(r.shape,s.shape,l,m,u,c,!0),y=V().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,x=d?yd(d,y):null,A=[r,s],b=i!=null,k=o!=null,S=d==="leakyrelu";if(b&&A.push(i),k&&A.push(o),S){let M=a.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));A.push(M),f.push(M)}let C;y?C=new ov(g,b,x,k,S):C=new iv(g,b,x,k,S);let N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=a.runWebGLProgram(C,A,"float32",N);return f.forEach(M=>a.disposeIntermediateTensorInfo(M)),$}var nX={kernelName:Xr,backendName:"webgl",kernelFunc:aX},rX=class{constructor(e,t,a,n){this.sliceDim=e,this.strides=t,this.paramsShape=n,this.variableNames=["x","indices"],this.outputShape=a;let r=gt(a.length),s=`
     int index;`;for(let i=0;i<this.sliceDim;i++)s+=`
           index = round(getIndices(coords[0], ${i}));
           out_of_bounds = out_of_bounds || index < 0;
@@ -3508,15 +3508,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${n}));
       }
-    `}};function lX(e,t){let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r<e.length;r++)r===2?n.push("index"):n.push(`${a[r]}`);return n.join()}function pv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0];if(V().get("DEBUG")){let x=a.readSync(s.dataId),A=r.shape[l];for(let b=0;b<x.length;++b){let k=x[b];v.assert(k<=A-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${A-1}]`)}}let u=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=v.sizeFromShape(s.shape),c=[],d=pe({inputs:{x:r},backend:a,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=pe({inputs:{x:s},backend:a,attrs:{shape:[u.batchSize,p/u.batchSize]}});c.push(d),c.push(h);let f=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(a.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let x=a.bufferSync(h),A=a.bufferSync(d),b=mU(A,x,f);return c.forEach(k=>a.disposeIntermediateTensorInfo(k)),a.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new oX(d.shape,f),g=a.runWebGLProgram(m,[d,h],d.dtype);c.push(g);let y=pe({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeIntermediateTensorInfo(x)),y}var uX={kernelName:Fl,backendName:"webgl",kernelFunc:pv},dX="return float(a > b);",pX=`
+    `}};function lX(e,t){let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r<e.length;r++)r===2?n.push("index"):n.push(`${a[r]}`);return n.join()}function dv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0];if(V().get("DEBUG")){let x=a.readSync(s.dataId),A=r.shape[l];for(let b=0;b<x.length;++b){let k=x[b];v.assert(k<=A-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${A-1}]`)}}let u=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=v.sizeFromShape(s.shape),c=[],d=pe({inputs:{x:r},backend:a,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=pe({inputs:{x:s},backend:a,attrs:{shape:[u.batchSize,p/u.batchSize]}});c.push(d),c.push(h);let f=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(a.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let x=a.bufferSync(h),A=a.bufferSync(d),b=mU(A,x,f);return c.forEach(k=>a.disposeIntermediateTensorInfo(k)),a.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new oX(d.shape,f),g=a.runWebGLProgram(m,[d,h],d.dtype);c.push(g);let y=pe({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeIntermediateTensorInfo(x)),y}var uX={kernelName:Fl,backendName:"webgl",kernelFunc:dv},dX="return float(a > b);",pX=`
   return vec4(greaterThan(a, b));
 `,cX=da({opSnippet:dX,packedOpSnippet:pX,cpuKernelImpl:gU,dtype:"bool"}),hX={kernelName:vi,backendName:"webgl",kernelFunc:cX},fX="return float(a >= b);",mX=`
   return vec4(greaterThanEqual(a, b));
-`,gX=da({opSnippet:fX,packedOpSnippet:mX,dtype:"bool",cpuKernelImpl:yU}),yX={kernelName:ki,backendName:"webgl",kernelFunc:gX};function xX(e){let{inputs:t,backend:a}=e,{input:n}=t;return dv(n,!0,a)}var AX={kernelName:Pd,backendName:"webgl",kernelFunc:xX},bX="return float(!isnan(x) && !isinf(x));",vX=Qe({opSnippet:bX,dtype:"bool"}),kX={kernelName:Ol,backendName:"webgl",kernelFunc:vX},wX="return float(isinf(x));",IX=Qe({opSnippet:wX,dtype:"bool"}),SX={kernelName:Dl,backendName:"webgl",kernelFunc:IX},TX="return float(isnan(x));",CX=Qe({opSnippet:TX,dtype:"bool"}),NX={kernelName:Ii,backendName:"webgl",kernelFunc:CX},EX="return float(a < b);",RX=`
+`,gX=da({opSnippet:fX,packedOpSnippet:mX,dtype:"bool",cpuKernelImpl:yU}),yX={kernelName:ki,backendName:"webgl",kernelFunc:gX};function xX(e){let{inputs:t,backend:a}=e,{input:n}=t;return uv(n,!0,a)}var AX={kernelName:_d,backendName:"webgl",kernelFunc:xX},bX="return float(!isnan(x) && !isinf(x));",vX=Qe({opSnippet:bX,dtype:"bool"}),kX={kernelName:Ol,backendName:"webgl",kernelFunc:vX},wX="return float(isinf(x));",IX=Qe({opSnippet:wX,dtype:"bool"}),SX={kernelName:Dl,backendName:"webgl",kernelFunc:IX},TX="return float(isnan(x));",CX=Qe({opSnippet:TX,dtype:"bool"}),NX={kernelName:Ii,backendName:"webgl",kernelFunc:CX},EX="return float(a < b);",RX=`
   return vec4(lessThan(a, b));
 `,MX=da({opSnippet:EX,packedOpSnippet:RX,cpuKernelImpl:xU,dtype:"bool"}),$X={kernelName:Ti,backendName:"webgl",kernelFunc:MX},_X="return float(a <= b);",PX=`
   return vec4(lessThanEqual(a, b));
-`,FX=da({opSnippet:_X,packedOpSnippet:PX,cpuKernelImpl:AU,dtype:"bool"}),OX={kernelName:Ci,backendName:"webgl",kernelFunc:FX};function DX(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=bU(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var zX={kernelName:Od,backendName:"webgl",kernelFunc:DX},LX=mu+`
+`,FX=da({opSnippet:_X,packedOpSnippet:PX,cpuKernelImpl:AU,dtype:"bool"}),OX={kernelName:Ci,backendName:"webgl",kernelFunc:FX};function DX(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=bU(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var zX={kernelName:Fd,backendName:"webgl",kernelFunc:DX},LX=mu+`
   return x < 0.0 ? 0./0. : log(x);
 `,BX=`
   vec4 result = log(x);
@@ -3618,7 +3618,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},sK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=V().getBool("WEBGL_PACK_NORMALIZATION")?new rK(r.shape,s,i,o,l):new nK(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},iK={kernelName:Dd,backendName:"webgl",kernelFunc:sK},oK=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=`
+    `}},sK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=V().getBool("WEBGL_PACK_NORMALIZATION")?new rK(r.shape,s,i,o,l):new nK(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},iK={kernelName:Od,backendName:"webgl",kernelFunc:sK},oK=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3673,16 +3673,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
       }
       setOutput(result);
       }
-    `}},lK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new oK(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},uK={kernelName:V2,backendName:"webgl",kernelFunc:lK};function dK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=vo(i,e.dtype,"max",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function cv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let x=a.texData.get(h.dataId).values,A=new Array(o);for(let S=0;S<A.length;S++)A[S]=r.shape[p[S]];let b=R3(x,r.shape,r.dtype,p,A);h=a.makeTensorInfo(A,r.dtype);let k=a.texData.get(h.dataId);k.values=b}else h=$h(r,p,a);u=T.getInnerMostAxes(u.length,o)}T.assertAxesAreInnerMostDims("max",u,o);let[f,m]=T.computeOutAndReduceShapes(h.shape,u),g=f;i&&(g=T.expandShapeToKeepDim(f,l));let y;if(d){let x=a.texData.get(h.dataId).values,A=kU(x,v.sizeFromShape(m),g,r.dtype);y=a.makeTensorInfo(g,r.dtype);let b=a.texData.get(y.dataId);b.values=A}else y=dK(h,m,g,a);return c&&a.disposeIntermediateTensorInfo(h),y}var pK={kernelName:$i,backendName:"webgl",kernelFunc:cv},cK=M3+`
+    `}},lK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new oK(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},uK={kernelName:V2,backendName:"webgl",kernelFunc:lK};function dK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=vo(i,e.dtype,"max",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function pv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let x=a.texData.get(h.dataId).values,A=new Array(o);for(let S=0;S<A.length;S++)A[S]=r.shape[p[S]];let b=R3(x,r.shape,r.dtype,p,A);h=a.makeTensorInfo(A,r.dtype);let k=a.texData.get(h.dataId);k.values=b}else h=Mh(r,p,a);u=T.getInnerMostAxes(u.length,o)}T.assertAxesAreInnerMostDims("max",u,o);let[f,m]=T.computeOutAndReduceShapes(h.shape,u),g=f;i&&(g=T.expandShapeToKeepDim(f,l));let y;if(d){let x=a.texData.get(h.dataId).values,A=kU(x,v.sizeFromShape(m),g,r.dtype);y=a.makeTensorInfo(g,r.dtype);let b=a.texData.get(y.dataId);b.values=A}else y=dK(h,m,g,a);return c&&a.disposeIntermediateTensorInfo(h),y}var pK={kernelName:$i,backendName:"webgl",kernelFunc:pv},cK=M3+`
   return max(a, b);
 `,hK=`
   vec4 result = vec4(max(a, b));
   bvec4 isNaNA = isnan(a);
   bvec4 isNaNB = isnan(b);
   bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
-  `+fp+`
+  `+hp+`
   return result;
-`,fK=da({opSnippet:cK,packedOpSnippet:hK,cpuKernelImpl:wU}),mK={kernelName:_i,backendName:"webgl",kernelFunc:fK};function gK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;uu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Ya({inputs:{x:r},backend:a});let c=new Ad(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var yK={kernelName:Pi,backendName:"webgl",kernelFunc:gK};function xK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new _3(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var AK={kernelName:nh,backendName:"webgl",kernelFunc:xK},bK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
+`,fK=da({opSnippet:cK,packedOpSnippet:hK,cpuKernelImpl:wU}),mK={kernelName:_i,backendName:"webgl",kernelFunc:fK};function gK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;uu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Ya({inputs:{x:r},backend:a});let c=new xd(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var yK={kernelName:Pi,backendName:"webgl",kernelFunc:gK};function xK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new _3(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var AK={kernelName:ah,backendName:"webgl",kernelFunc:xK},bK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3792,14 +3792,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function kK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=T.computePool3DInfo(i.shape,o,l,c,u,p),h=new _3(d,"max",!0),f=a.runWebGLProgram(h,[i],i.dtype),m=new vK(d),g=a.runWebGLProgram(m,[r,f],i.dtype);return a.disposeIntermediateTensorInfo(f),g}var wK={kernelName:G2,backendName:"webgl",kernelFunc:kK};function IK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;uu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=T.computePool2DInfo(o.shape,l,u,1,p,c),h=!0,f=new Ad(d,"max",h),m=a.runWebGLProgram(f,[o],o.dtype),g=new bK(d),y=a.runWebGLProgram(g,[r,m],o.dtype);return a.disposeIntermediateTensorInfo(m),y}var SK={kernelName:U2,backendName:"webgl",kernelFunc:IK};function TK(e,t,a,n){let r=new Ad(a,"max",!1),s=n.runWebGLProgram(r,[e],"float32");r=new Ad(a,"max",!0,!0,t);let i=n.runWebGLProgram(r,[e],"float32");return[s,i]}var CK={kernelName:rh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(n.shape,r,s,u,i),[c,d]=TK(n,o,p,l);return[c,d]}};function NK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=vo(i,"float32","mean",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var EK={kernelName:Fi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{keepDims:r,axis:s}=t,i=a,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=i.shouldExecuteOnCPU([n]),h=[],f=n;if(c){if(d){let A=i.texData.get(f.dataId).values,b=new Array(o);for(let C=0;C<b.length;C++)b[C]=n.shape[p[C]];let k=R3(A,n.shape,n.dtype,p,b);f=i.makeTensorInfo(b,n.dtype);let S=i.texData.get(f.dataId);S.values=k}else f=$h(n,p,i);h.push(f),u=T.getInnerMostAxes(u.length,o)}T.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=T.computeOutAndReduceShapes(f.shape,u),y=m;r&&(y=T.expandShapeToKeepDim(m,l));let x=NK(f,g,y,i);for(let A of h)i.disposeIntermediateTensorInfo(A);return x}};function RK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Sa({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,r.shape.length)),T.assertAxesAreInnerMostDims("min",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"min",a),y;if(i){let x=T.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var MK={kernelName:Oi,backendName:"webgl",kernelFunc:RK},$K=M3+`
+    `}};function kK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=T.computePool3DInfo(i.shape,o,l,c,u,p),h=new _3(d,"max",!0),f=a.runWebGLProgram(h,[i],i.dtype),m=new vK(d),g=a.runWebGLProgram(m,[r,f],i.dtype);return a.disposeIntermediateTensorInfo(f),g}var wK={kernelName:G2,backendName:"webgl",kernelFunc:kK};function IK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;uu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=T.computePool2DInfo(o.shape,l,u,1,p,c),h=!0,f=new xd(d,"max",h),m=a.runWebGLProgram(f,[o],o.dtype),g=new bK(d),y=a.runWebGLProgram(g,[r,m],o.dtype);return a.disposeIntermediateTensorInfo(m),y}var SK={kernelName:U2,backendName:"webgl",kernelFunc:IK};function TK(e,t,a,n){let r=new xd(a,"max",!1),s=n.runWebGLProgram(r,[e],"float32");r=new xd(a,"max",!0,!0,t);let i=n.runWebGLProgram(r,[e],"float32");return[s,i]}var CK={kernelName:nh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(n.shape,r,s,u,i),[c,d]=TK(n,o,p,l);return[c,d]}};function NK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=vo(i,"float32","mean",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var EK={kernelName:Fi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{keepDims:r,axis:s}=t,i=a,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=i.shouldExecuteOnCPU([n]),h=[],f=n;if(c){if(d){let A=i.texData.get(f.dataId).values,b=new Array(o);for(let C=0;C<b.length;C++)b[C]=n.shape[p[C]];let k=R3(A,n.shape,n.dtype,p,b);f=i.makeTensorInfo(b,n.dtype);let S=i.texData.get(f.dataId);S.values=k}else f=Mh(n,p,i);h.push(f),u=T.getInnerMostAxes(u.length,o)}T.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=T.computeOutAndReduceShapes(f.shape,u),y=m;r&&(y=T.expandShapeToKeepDim(m,l));let x=NK(f,g,y,i);for(let A of h)i.disposeIntermediateTensorInfo(A);return x}};function RK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Sa({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,r.shape.length)),T.assertAxesAreInnerMostDims("min",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"min",a),y;if(i){let x=T.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var MK={kernelName:Oi,backendName:"webgl",kernelFunc:RK},$K=M3+`
   return min(a, b);
 `,_K=`
   vec4 result = vec4(min(a, b));
   bvec4 isNaNA = isnan(a);
   bvec4 isNaNB = isnan(b);
   bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
-  `+fp+`
+  `+hp+`
   return result;
 `,PK=da({opSnippet:$K,packedOpSnippet:_K,cpuKernelImpl:IU}),FK={kernelName:Di,backendName:"webgl",kernelFunc:PK},OK=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let n=e.length,r=gt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=a==="reflect"?0:1;if(n===1){this.userCode=`
         int start = ${s};
@@ -3890,7 +3890,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelNam
   return mod(a, b);`,WK=`
   vec4 result = mod(a, b);
   bvec4 isNaN = equal(b, vec4(0.0));
-  `+fp+`
+  `+hp+`
   return result;
 `,VK=da({opSnippet:BK,packedOpSnippet:WK}),UK={kernelName:Ll,backendName:"webgl",kernelFunc:VK},GK=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],this.userCode=`
       void main() {
@@ -3934,7 +3934,7 @@ return a / b;`,jK=`
   }
 
   return result;
-`,hv=da({opSnippet:HK,packedOpSnippet:jK,checkOutOfBounds:!0}),qK={kernelName:ci,backendName:"webgl",kernelFunc:hv},Ry="return a - b;",fv=da({opSnippet:Ry,packedOpSnippet:Ry,supportsComplex:!0,cpuKernelImpl:GU}),XK={kernelName:po,backendName:"webgl",kernelFunc:fv};function mv(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=cv({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=pe({inputs:{x:o},backend:a,attrs:{shape:l}}),p=fv({inputs:{a:r,b:u},backend:a}),c=uv({inputs:{x:p},backend:a}),d=_h({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=pe({inputs:{x:d},backend:a,attrs:{shape:l}}),f=hv({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}var KK={kernelName:oo,backendName:"webgl",kernelFunc:mv};function ZK(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:mv({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new GK(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var YK={kernelName:sh,backendName:"webgl",kernelFunc:ZK},JK=Nn+`
+`,cv=da({opSnippet:HK,packedOpSnippet:jK,checkOutOfBounds:!0}),qK={kernelName:ci,backendName:"webgl",kernelFunc:cv},Ey="return a - b;",hv=da({opSnippet:Ey,packedOpSnippet:Ey,supportsComplex:!0,cpuKernelImpl:GU}),XK={kernelName:po,backendName:"webgl",kernelFunc:hv};function fv(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=pv({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=pe({inputs:{x:o},backend:a,attrs:{shape:l}}),p=hv({inputs:{a:r,b:u},backend:a}),c=lv({inputs:{x:p},backend:a}),d=$h({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=pe({inputs:{x:d},backend:a,attrs:{shape:l}}),f=cv({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}var KK={kernelName:oo,backendName:"webgl",kernelFunc:fv};function ZK(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:fv({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new GK(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var YK={kernelName:rh,backendName:"webgl",kernelFunc:ZK},JK=Nn+`
   return -x;
 `,QK=`
   vec4 result = -x;
@@ -3953,7 +3953,7 @@ return a / b;`,jK=`
         setOutput(mix(float(${n}), float(${a}),
                       float(index == coords.y)));
       }
-    `}},cZ=e=>{let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new pZ(u,i,o,l),c=pe({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=a.runWebGLProgram(p,[c],s);a.disposeIntermediateTensorInfo(c);let h=[...r.shape,i],f=pe({inputs:{x:d},backend:a,attrs:{shape:h}});return a.disposeIntermediateTensorInfo(d),f},hZ={kernelName:Ui,backendName:"webgl",kernelFunc:cZ};function Vc(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=gp({inputs:{input:n},backend:a}),s=Vc({inputs:{x:r},backend:a}),i=Ph({inputs:{input:n},backend:a}),o=Vc({inputs:{x:i},backend:a}),l=us({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return yp({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var fZ={kernelName:nu,backendName:"webgl",kernelFunc:Vc};function gv(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=gp({inputs:{input:n},backend:a}),s=gv({inputs:{x:r},backend:a}),i=Ph({inputs:{input:n},backend:a}),o=Vc({inputs:{x:i},backend:a}),l=us({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return yp({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var mZ={kernelName:Vl,backendName:"webgl",kernelFunc:gv};function gZ(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return I2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=I2({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=tv({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var yZ={kernelName:Ul,backendName:"webgl",kernelFunc:gZ},xZ=class{constructor(e,t,a){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,r=gt(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=`
+    `}},cZ=e=>{let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new pZ(u,i,o,l),c=pe({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=a.runWebGLProgram(p,[c],s);a.disposeIntermediateTensorInfo(c);let h=[...r.shape,i],f=pe({inputs:{x:d},backend:a,attrs:{shape:h}});return a.disposeIntermediateTensorInfo(d),f},hZ={kernelName:Ui,backendName:"webgl",kernelFunc:cZ};function Wc(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=mp({inputs:{input:n},backend:a}),s=Wc({inputs:{x:r},backend:a}),i=_h({inputs:{input:n},backend:a}),o=Wc({inputs:{x:i},backend:a}),l=us({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return gp({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var fZ={kernelName:nu,backendName:"webgl",kernelFunc:Wc};function mv(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=mp({inputs:{input:n},backend:a}),s=mv({inputs:{x:r},backend:a}),i=_h({inputs:{input:n},backend:a}),o=Wc({inputs:{x:i},backend:a}),l=us({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return gp({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var mZ={kernelName:Vl,backendName:"webgl",kernelFunc:mv};function gZ(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return I2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=I2({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=ev({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var yZ={kernelName:Ul,backendName:"webgl",kernelFunc:gZ},xZ=class{constructor(e,t,a){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,r=gt(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};
 
@@ -4002,7 +4002,7 @@ return a / b;`,jK=`
         ${h}
         setOutput(result);
       }
-    `}},yv=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return yp({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AZ(r.shape,s,i):new xZ(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},bZ={kernelName:Gi,backendName:"webgl",kernelFunc:yv},vZ=`
+    `}},gv=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return gp({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AZ(r.shape,s,i):new xZ(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},bZ={kernelName:Gi,backendName:"webgl",kernelFunc:gv},vZ=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -4027,9 +4027,9 @@ return a / b;`,jK=`
   bvec4 isNaN1 = lessThan(a, vec4(0.0));
   bvec4 isNaN2 = lessThan(floor(b), b);
   bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
-  `+fp+`
+  `+hp+`
   return result;
-`,wZ=da({opSnippet:vZ,packedOpSnippet:kZ}),IZ={kernelName:Hi,backendName:"webgl",kernelFunc:wZ};function SZ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,c=T.getAxesPermutation(p,o),d=r;c!=null&&(d=Sa({inputs:{x:r},backend:a,attrs:{perm:c}}),p=T.getInnerMostAxes(p.length,o),l.push(d)),T.assertAxesAreInnerMostDims("prod",p,o);let h;if(a.shouldExecuteOnCPU([d])){let f=a.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:y}=NU(d.shape,d.dtype,f,p);h=a.makeTensorInfo(g,y,m)}else{let[f,m]=T.computeOutAndReduceShapes(d.shape,p),g=v.sizeFromShape(m),y=pe({inputs:{x:d},backend:a,attrs:{shape:[-1,g]}}),x=Kd(r.dtype),A=vo(y,x,"prod",a);h=pe({inputs:{x:A},backend:a,attrs:{shape:f}}),l.push(y),l.push(A)}if(i){l.push(h);let f=T.expandShapeToKeepDim(h.shape,u);h=pe({inputs:{x:h},backend:a,attrs:{shape:f}})}return l.forEach(f=>a.disposeIntermediateTensorInfo(f)),h}var TZ={kernelName:qi,backendName:"webgl",kernelFunc:SZ};function CZ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(y=>a.readSync(y.dataId)),u=r.map(y=>y.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,f]=EU(l,u,p,s.shape,s.dtype,c,i.shape,o),m=d.map(y=>a.makeTensorInfo([y.length],"int32",y)),g=a.makeTensorInfo(f,s.dtype,h);return m.concat([g])}var NZ={kernelName:ih,backendName:"webgl",kernelFunc:CZ};function EZ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=RU(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var RZ={kernelName:oh,backendName:"webgl",kernelFunc:EZ};function MZ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[f,m]=MU(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(f,s.dtype,m)}var $Z={kernelName:lh,backendName:"webgl",kernelFunc:MZ},xv=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=$U(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},_Z={kernelName:Gl,backendName:"webgl",kernelFunc:xv},PZ="return 1.0 / x;",FZ=Qe({opSnippet:PZ}),OZ={kernelName:Xi,backendName:"webgl",kernelFunc:FZ},DZ=Nn+`
+`,wZ=da({opSnippet:vZ,packedOpSnippet:kZ}),IZ={kernelName:Hi,backendName:"webgl",kernelFunc:wZ};function SZ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,c=T.getAxesPermutation(p,o),d=r;c!=null&&(d=Sa({inputs:{x:r},backend:a,attrs:{perm:c}}),p=T.getInnerMostAxes(p.length,o),l.push(d)),T.assertAxesAreInnerMostDims("prod",p,o);let h;if(a.shouldExecuteOnCPU([d])){let f=a.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:y}=NU(d.shape,d.dtype,f,p);h=a.makeTensorInfo(g,y,m)}else{let[f,m]=T.computeOutAndReduceShapes(d.shape,p),g=v.sizeFromShape(m),y=pe({inputs:{x:d},backend:a,attrs:{shape:[-1,g]}}),x=Xd(r.dtype),A=vo(y,x,"prod",a);h=pe({inputs:{x:A},backend:a,attrs:{shape:f}}),l.push(y),l.push(A)}if(i){l.push(h);let f=T.expandShapeToKeepDim(h.shape,u);h=pe({inputs:{x:h},backend:a,attrs:{shape:f}})}return l.forEach(f=>a.disposeIntermediateTensorInfo(f)),h}var TZ={kernelName:qi,backendName:"webgl",kernelFunc:SZ};function CZ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(y=>a.readSync(y.dataId)),u=r.map(y=>y.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,f]=EU(l,u,p,s.shape,s.dtype,c,i.shape,o),m=d.map(y=>a.makeTensorInfo([y.length],"int32",y)),g=a.makeTensorInfo(f,s.dtype,h);return m.concat([g])}var NZ={kernelName:sh,backendName:"webgl",kernelFunc:CZ};function EZ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=RU(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var RZ={kernelName:ih,backendName:"webgl",kernelFunc:EZ};function MZ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[f,m]=MU(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(f,s.dtype,m)}var $Z={kernelName:oh,backendName:"webgl",kernelFunc:MZ},yv=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=$U(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},_Z={kernelName:Gl,backendName:"webgl",kernelFunc:yv},PZ="return 1.0 / x;",FZ=Qe({opSnippet:PZ}),OZ={kernelName:Xi,backendName:"webgl",kernelFunc:FZ},DZ=Nn+`
   return (x < 0.0) ? 0.0 : x;
 `,zZ=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
@@ -4449,7 +4449,7 @@ return a / b;`,jK=`
       return base + 1.0;
     }
   }
-`,cY=Qe({opSnippet:pY}),hY={kernelName:eo,backendName:"webgl",kernelFunc:cY},fY="return inversesqrt(x);",mY=Qe({opSnippet:fY,cpuKernelImpl:_U}),gY={kernelName:to,backendName:"webgl",kernelFunc:mY},Av=class{constructor(e,t,a,n,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),u="";a===1?u="i":a===2&&(u="i, j");let p=`getIndices(${u})`,c="";n===1?c="i":n===2&&(c="i, coords[1]");let d=`getUpdates(${c})`,h=t>1?"strides[j]":"strides";this.userCode=`
+`,cY=Qe({opSnippet:pY}),hY={kernelName:eo,backendName:"webgl",kernelFunc:cY},fY="return inversesqrt(x);",mY=Qe({opSnippet:fY,cpuKernelImpl:_U}),gY={kernelName:to,backendName:"webgl",kernelFunc:mY},xv=class{constructor(e,t,a,n,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),u="";a===1?u="i":a===2&&(u="i, j");let p=`getIndices(${u})`,c="";n===1?c="i":n===2&&(c="i, coords[1]");let d=`getUpdates(${c})`,h=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${r});
 
         void main() {
@@ -4469,7 +4469,7 @@ return a / b;`,jK=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function yY(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=pe({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=pe({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=a.makeTensorInfo([],"float32",new Float32Array([0])),g=new Av(l,o,h.shape.length,f.shape.length,p,d),y=a.runWebGLProgram(g,[f,h,m],f.dtype),x=pe({inputs:{x:y},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(m),x}var xY={kernelName:ao,backendName:"webgl",kernelFunc:yY},AY=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=V().getNumber("WEBGL_VERSION")===2?r:s,o=n==="left"?"<":"<=";this.userCode=`
+      `}};function yY(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=pe({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=pe({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=a.makeTensorInfo([],"float32",new Float32Array([0])),g=new xv(l,o,h.shape.length,f.shape.length,p,d),y=a.runWebGLProgram(g,[f,h,m],f.dtype),x=pe({inputs:{x:y},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(m),x}var xY={kernelName:ao,backendName:"webgl",kernelFunc:yY},AY=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=V().getNumber("WEBGL_VERSION")===2?r:s,o=n==="left"?"<":"<=";this.userCode=`
        int findBound(int batch, float value) {
          int left = 0;
          int right = numInputs;
@@ -4494,7 +4494,7 @@ return a / b;`,jK=`
 
          setOutput(float(findBound(batch, value)));
        }
-     `}};function bY(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new AY(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return a.runWebGLProgram(o,[r,s],"int32",l)}var vY={kernelName:Ld,backendName:"webgl",kernelFunc:bY},kY=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.outputShape=t;let n,r;if(a>4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),r=l.join()}let s=gt(a);this.userCode=`
+     `}};function bY(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new AY(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return a.runWebGLProgram(o,[r,s],"int32",l)}var vY={kernelName:zd,backendName:"webgl",kernelFunc:bY},kY=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.outputShape=t;let n,r;if(a>4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),r=l.join()}let s=gt(a);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${n});
@@ -4550,15 +4550,15 @@ return a / b;`,jK=`
     result = log(exp_x + 1.0);
   }
   return result;
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               ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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+              ${s.shape}`);let i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=B6(i,n.shape,n.dtype,o,l,!0);return a.makeTensorInfo(p,n.dtype,u)}var YY={kernelName:Bd,backendName:"webgl",kernelFunc:ZY};function JY(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.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(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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+             ${s.shape}`);let i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=B6(i,n.shape,n.dtype,o,l);return a.makeTensorInfo(p,n.dtype,u)}var QY={kernelName:Wd,backendName:"webgl",kernelFunc:JY};function eJ(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=a.bufferSync(r),x=a.bufferSync(s),A=v.decodeString(a.readSync(i.dataId)[0]),b=PU(y,x,o,d,p,u,l,c,A,h);return a.makeTensorInfo(o,b.dtype,b.values)}let f=new xv(u,l,r.shape.length,s.shape.length,c,[d,1],h),m=a.runWebGLProgram(f,[s,r,i],s.dtype),g=pe({inputs:{x:m},backend:a,attrs:{shape:o}});return a.disposeIntermediateTensorInfo(m),g}var tJ={kernelName:Vd,backendName:"webgl",kernelFunc:eJ};function aJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let f=gu({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,f})}var nJ={kernelName:Ql,backendName:"webgl",kernelFunc:aJ},Ry="return sqrt(x);",rJ=Qe({opSnippet:Ry,packedOpSnippet:Ry,cpuKernelImpl:LU}),sJ={kernelName:so,backendName:"webgl",kernelFunc:rJ},iJ="return x * x;",oJ=Qe({opSnippet:iJ}),lJ={kernelName:Ud,backendName:"webgl",kernelFunc:oJ},My="return (a - b) * (a - b);",uJ=da({opSnippet:My,packedOpSnippet:My}),dJ={kernelName:lo,backendName:"webgl",kernelFunc:uJ};function pJ({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=Nn+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
   `,s=new qn(n.shape,r);return a.runWebGLProgram(s,[n],n.dtype)}var cJ={kernelName:ss,backendName:"webgl",kernelFunc:pJ},hJ=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=gt(a.length),s=gt(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
       ${r} begin = ${r}(${e});
@@ -4568,7 +4568,7 @@ return a / b;`,jK=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function fJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=St.sliceInfo(r.shape,s,i,o,l,u,p,c,d),k;if(m)k=pe({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=St.computeOutShape(x,A,b),N=gu({inputs:{x:r},backend:a,attrs:{begin:x,size:C}});k=pe({inputs:{x:N},backend:a,attrs:{shape:f}}),a.disposeIntermediateTensorInfo(N)}else if(a.shouldExecuteOnCPU([r])){let C=a.readSync(r.dataId),N=_e(r.shape,r.dtype,C),$=BU(h,N,b,x);k=a.makeTensorInfo(f,r.dtype,$.values)}else{let C=new hJ(x,b,h);k=a.runWebGLProgram(C,[r],r.dtype)}let S=pe({inputs:{x:k},backend:a,attrs:{shape:f}});return a.disposeIntermediateTensorInfo(k),S}var mJ={kernelName:uo,backendName:"webgl",kernelFunc:fJ};function gJ(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[f,m]=WU(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([f.length],"string",f),a.makeTensorInfo(c.shape,"int32",m)]}var yJ={kernelName:tu,backendName:"webgl",kernelFunc:gJ};function xJ(e){let{inputs:t,backend:a,attrs:n}=e,{skipEmpty:r}=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=a.readSync(s.dataId),l=a.readSync(i.dataId)[0],[u,p,c]=VU(o,l,r),d=p.length;return[a.makeTensorInfo([d,2],"int32",u),a.makeTensorInfo([d],"string",p),a.makeTensorInfo([2],"int32",new Int32Array(c))]}var AJ={kernelName:Hd,backendName:"webgl",kernelFunc:xJ};function bJ(e){let{inputs:t,backend:a,attrs:n}=e,{numBuckets:r}=n,{input:s}=t;if(s.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=a.readSync(s.dataId),o=UU(i,r);return a.makeTensorInfo(s.shape,"int32",o)}var vJ={kernelName:jd,backendName:"webgl",kernelFunc:bJ},kJ="return tan(x);",wJ=Qe({opSnippet:kJ}),IJ={kernelName:co,backendName:"webgl",kernelFunc:wJ},SJ=`
+    `}};function fJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=St.sliceInfo(r.shape,s,i,o,l,u,p,c,d),k;if(m)k=pe({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=St.computeOutShape(x,A,b),N=gu({inputs:{x:r},backend:a,attrs:{begin:x,size:C}});k=pe({inputs:{x:N},backend:a,attrs:{shape:f}}),a.disposeIntermediateTensorInfo(N)}else if(a.shouldExecuteOnCPU([r])){let C=a.readSync(r.dataId),N=_e(r.shape,r.dtype,C),$=BU(h,N,b,x);k=a.makeTensorInfo(f,r.dtype,$.values)}else{let C=new hJ(x,b,h);k=a.runWebGLProgram(C,[r],r.dtype)}let S=pe({inputs:{x:k},backend:a,attrs:{shape:f}});return a.disposeIntermediateTensorInfo(k),S}var mJ={kernelName:uo,backendName:"webgl",kernelFunc:fJ};function gJ(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[f,m]=WU(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([f.length],"string",f),a.makeTensorInfo(c.shape,"int32",m)]}var yJ={kernelName:tu,backendName:"webgl",kernelFunc:gJ};function xJ(e){let{inputs:t,backend:a,attrs:n}=e,{skipEmpty:r}=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=a.readSync(s.dataId),l=a.readSync(i.dataId)[0],[u,p,c]=VU(o,l,r),d=p.length;return[a.makeTensorInfo([d,2],"int32",u),a.makeTensorInfo([d],"string",p),a.makeTensorInfo([2],"int32",new Int32Array(c))]}var AJ={kernelName:Gd,backendName:"webgl",kernelFunc:xJ};function bJ(e){let{inputs:t,backend:a,attrs:n}=e,{numBuckets:r}=n,{input:s}=t;if(s.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=a.readSync(s.dataId),o=UU(i,r);return a.makeTensorInfo(s.shape,"int32",o)}var vJ={kernelName:Hd,backendName:"webgl",kernelFunc:bJ},kJ="return tan(x);",wJ=Qe({opSnippet:kJ}),IJ={kernelName:co,backendName:"webgl",kernelFunc:wJ},SJ=`
   float e2x = exp(-2.0 * abs(x));
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 `,TJ=Qe({opSnippet:SJ}),CJ={kernelName:ho,backendName:"webgl",kernelFunc:TJ},NJ=class{constructor(e,t){this.variableNames=["A"];let a=new Array(e.length);for(let s=0;s<a.length;s++)a[s]=e[s]*t[s];this.outputShape=a,this.rank=a.length;let n=gt(this.rank),r=EJ(e);this.userCode=`
@@ -4576,7 +4576,7 @@ return a / b;`,jK=`
         ${n} resRC = getOutputCoords();
         setOutput(getA(${r}));
       }
-    `}};function EJ(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let r=0;r<e.length;r++)n.push(`imod(${a[r]}, ${e[r]})`);return n.join()}function bv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reps:s}=n;if(r.dtype==="string"||r.shape.length>5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=HU(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new NJ(r.shape,s);return a.runWebGLProgram(i,[r],r.dtype)}var RJ={kernelName:rs,backendName:"webgl",kernelFunc:bv},MJ=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 EJ(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let r=0;r<e.length;r++)n.push(`imod(${a[r]}, ${e[r]})`);return n.join()}function Av(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reps:s}=n;if(r.dtype==="string"||r.shape.length>5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=HU(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new NJ(r.shape,s);return a.runWebGLProgram(i,[r],r.dtype)}var RJ={kernelName:rs,backendName:"webgl",kernelFunc:Av},MJ=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];
@@ -4650,7 +4650,7 @@ return a / b;`,jK=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function Rs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function _y(e){let t=1;for(;t<e;)t*=2;return t}function _J(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{k:s,sorted:i}=n,o=V().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=V().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,p=u[u.length-1];if(a.shouldExecuteOnCPU([r])||p<o||s>l){let $=a.readSync(r.dataId),[M,R]=jU($,u,r.dtype,s,i);return[a.makeTensorInfo(M.shape,M.dtype,M.values),a.makeTensorInfo(R.shape,R.dtype,R.values)]}if(s===0)return u[u.length-1]=0,[a.makeTensorInfo(u,r.dtype,[]),a.makeTensorInfo(u,"int32",[])];if(p===1)return[r,yp({attrs:{shape:u,dtype:"int32",value:0},backend:a})];let c=a.texData.get(r.dataId),d=c!==null&&c.isPacked,h=d?a.unpackTensor(r):r,f=v.sizeFromShape(u)/p,m=pe({inputs:{x:h},attrs:{shape:[f,p]},backend:a});d&&Rs(a,h);let g=_y(s),y=_y(p),x=null,A=()=>x===null?[m,m]:[m,x],b=($,M,R)=>{let I=A(),_=new MJ(R),D=[[p],[x===null?1:0],[Number.NEGATIVE_INFINITY],[$],[M]],W=x;x=a.runWebGLProgram(_,I,"int32",D),Rs(a,W)};for(let $=1;$<g;$*=2){let M=$*2;for(let R=$;R>=1;R/=2)b(M,R,[f,y])}for(let $=y;$>g;$/=2){let M=A(),R=new $J([f,$/2]),I=[[p],[x===null?1:0],[g]],_=x;x=a.runWebGLProgram(R,M,"int32",I),Rs(a,_);let D=g/2,W=D*2;for(let P=D;P>=1;P/=2)b(W,P,x.shape)}let k=x;x=gu({inputs:{x},backend:a,attrs:{begin:0,size:[f,s]}}),Rs(a,k);let S=pv({inputs:{x:m,indices:x},backend:a,attrs:{axis:1,batchDims:1}});Rs(a,m);let C=u.slice(0,-1);C.push(s),k=x,x=pe({inputs:{x},attrs:{shape:C},backend:a}),Rs(a,k);let N=S;return S=pe({inputs:{x:S},attrs:{shape:C},backend:a}),Rs(a,N),[S,x]}var PJ={kernelName:fo,backendName:"webgl",kernelFunc:_J},FJ=class{constructor(e,t,a,n,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=a==="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=`
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               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4762,7 +4762,7 @@ return a / b;`,jK=`
               }
               setOutput(outputValue);
             }
-        `}};function OJ(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,c,d,h]=r.shape,[f,m]=u!=null?u:[c,d],g=[p,f,m,h],y=new FJ(c,d,i,o,l,g);return a.runWebGLProgram(y,[r,s],"float32")}var DJ={kernelName:mo,backendName:"webgl",kernelFunc:OJ};function zJ(e){let{inputs:t,attrs:a,backend:n}=e,{axis:r}=a,{x:s}=t;uu(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}=qU(i,r,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var LJ={kernelName:uh,backendName:"webgl",kernelFunc:zJ};function BJ(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let m=0;m<o;m++)m!==s&&(u[p++]=i.shape[m]);let c=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let g=gu({inputs:{x:i},backend:a,attrs:{begin:d,size:h}}),y=pe({inputs:{x:g},backend:a,attrs:{shape:u}});f[m]=y,c.push(g)}return c.forEach(m=>a.disposeIntermediateTensorInfo(m)),f}var WJ={kernelName:au,backendName:"webgl",kernelFunc:BJ},VJ=class{constructor(e,t){this.variableNames=["x","segmentIds"];let a=e.windowSize,n=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/a);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(a/4)*4,p=a%4,c=`
+        `}};function OJ(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,c,d,h]=r.shape,[f,m]=u!=null?u:[c,d],g=[p,f,m,h],y=new FJ(c,d,i,o,l,g);return a.runWebGLProgram(y,[r,s],"float32")}var DJ={kernelName:mo,backendName:"webgl",kernelFunc:OJ};function zJ(e){let{inputs:t,attrs:a,backend:n}=e,{axis:r}=a,{x:s}=t;uu(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}=qU(i,r,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var LJ={kernelName:lh,backendName:"webgl",kernelFunc:zJ};function BJ(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let m=0;m<o;m++)m!==s&&(u[p++]=i.shape[m]);let c=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let g=gu({inputs:{x:i},backend:a,attrs:{begin:d,size:h}}),y=pe({inputs:{x:g},backend:a,attrs:{shape:u}});f[m]=y,c.push(g)}return c.forEach(m=>a.disposeIntermediateTensorInfo(m)),f}var WJ={kernelName:au,backendName:"webgl",kernelFunc:BJ},VJ=class{constructor(e,t){this.variableNames=["x","segmentIds"];let a=e.windowSize,n=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/a);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(a/4)*4,p=a%4,c=`
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@@ -4868,9 +4868,9 @@ return a / b;`,jK=`
         }
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zQ(e){let{backend:t,inputs:a,attrs:n}=e,{dy:r,filter:s}=a,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:p}=n,c=1,d=T.convertConv2DDataFormat(l),h=T.computeConv2DInfo(p,s.shape,i,c,o,u,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:x,inWidth:A,outChannels:b,outHeight:k,outWidth:S,strideHeight:C,strideWidth:N}=h,$=m-1-h.padInfo.top,M=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",I=v.computeStrides(h.inShape),_=v.computeStrides(r.shape),[D,W,P]=v.computeStrides(s.shape),U=I[0],G=R?I[1]:I[2],q=R?I[2]:1,H=R?1:I[1],B=_[0],Z=R?_[1]:_[2],X=R?_[2]:1,ne=R?1:_[1],ee=t.makeOutput(h.inShape,"float32"),ce=t.dataIdMap.get(ee.dataId).id,ie=t.dataIdMap.get(r.dataId).id,me=t.dataIdMap.get(s.dataId).id;return $v(ie,me,f,m,g,x,A,y,k,S,b,C,N,$,M,D,W,P,U,G,q,H,B,Z,X,ne,ce),ee}var LQ={kernelName:ri,backendName:"wasm",setupFunc:DQ,kernelFunc:zQ},BQ=Bt(si),WQ=Bt(ii),S2;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(S2||(S2={}));var _v;function 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JQ(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),A=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return Ov(g,s,i==="NHWC"?1:0,y,r.shape.length-1,x,A,f.length,b),m}var QQ={kernelName:di,backendName:"wasm",setupFunc:YQ,kernelFunc:JQ},Dv;function eee(e){Dv=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tee(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,k=h.dilationWidth,S=h.strideHeight,C=h.strideWidth,N=h.inChannels,$=h.outChannels,M=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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ute={kernelName:Oi,backendName:"wasm",setupFunc:ote,kernelFunc:lte},dte=!1,pte=pa(Di,dte),C2;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(C2||(C2={}));var Zv;function cte(e){Zv=e.wasm.cwrap(zi,null,["number","array","number","number","array","array","number","number"])}function hte(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(f=>f[0]),c=n.map(f=>f[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return Zv(i,u,t.shape.length,Ct[t.dtype],d,h,C2[r],l),o}var fte={kernelName:zi,backendName:"wasm",kernelFunc:hte,setupFunc:cte},mte=!0,gte=pa(Li,mte),yte=Bt(Bl);function P3(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var Yv;function xte(e){Yv=e.wasm.cwrap(Wi,"number",["number","number","number","number","number"])}function Ate(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=Yv(u,p,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=P3(t,c);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var bte={kernelName:Wi,backendName:"wasm",setupFunc:xte,kernelFunc:Ate},Jv;function vte(e){Jv=e.wasm.cwrap(Wl,"number",["number","number","number","number","number","bool"])}function kte(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Jv(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=P3(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var wte={kernelName:Wl,backendName:"wasm",setupFunc:vte,kernelFunc:kte},Qv;function Ite(e){Qv=e.wasm.cwrap(Vi,"number",["number","number","number","number","number","number"])}function Ste(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Qv(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=P3(t,d);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[y,x]}var Tte={kernelName:Vi,backendName:"wasm",setupFunc:Ite,kernelFunc:Ste},Cte=!1,Nte=pa(Bi,Cte,"bool"),e8;function Ete(e){e8=e.wasm.cwrap(Ui,null,["number","number","number","number","number"])}function Rte(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),p=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return e8(c,i,o,l,p),u}var Mte={kernelName:Ui,backendName:"wasm",setupFunc:Ete,kernelFunc:Rte};function $te(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var _te={kernelName:Vl,backendName:"wasm",kernelFunc:$te};function Pte(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return T2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching 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zQ(e){let{backend:t,inputs:a,attrs:n}=e,{dy:r,filter:s}=a,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:p}=n,c=1,d=T.convertConv2DDataFormat(l),h=T.computeConv2DInfo(p,s.shape,i,c,o,u,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:x,inWidth:A,outChannels:b,outHeight:k,outWidth:S,strideHeight:C,strideWidth:N}=h,$=m-1-h.padInfo.top,M=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",I=v.computeStrides(h.inShape),_=v.computeStrides(r.shape),[D,W,P]=v.computeStrides(s.shape),U=I[0],G=R?I[1]:I[2],q=R?I[2]:1,H=R?1:I[1],B=_[0],Z=R?_[1]:_[2],X=R?_[2]:1,ne=R?1:_[1],ee=t.makeOutput(h.inShape,"float32"),ce=t.dataIdMap.get(ee.dataId).id,ie=t.dataIdMap.get(r.dataId).id,me=t.dataIdMap.get(s.dataId).id;return Mv(ie,me,f,m,g,x,A,y,k,S,b,C,N,$,M,D,W,P,U,G,q,H,B,Z,X,ne,ce),ee}var LQ={kernelName:ri,backendName:"wasm",setupFunc:DQ,kernelFunc:zQ},BQ=Bt(si),WQ=Bt(ii),S2;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(S2||(S2={}));var $v;function 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JQ(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),A=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return Fv(g,s,i==="NHWC"?1:0,y,r.shape.length-1,x,A,f.length,b),m}var QQ={kernelName:di,backendName:"wasm",setupFunc:YQ,kernelFunc:JQ},Ov;function eee(e){Ov=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tee(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,k=h.dilationWidth,S=h.strideHeight,C=h.strideWidth,N=h.inChannels,$=h.outChannels,M=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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ute={kernelName:Oi,backendName:"wasm",setupFunc:ote,kernelFunc:lte},dte=!1,pte=pa(Di,dte),C2;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(C2||(C2={}));var Kv;function cte(e){Kv=e.wasm.cwrap(zi,null,["number","array","number","number","array","array","number","number"])}function hte(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(f=>f[0]),c=n.map(f=>f[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return Kv(i,u,t.shape.length,Ct[t.dtype],d,h,C2[r],l),o}var fte={kernelName:zi,backendName:"wasm",kernelFunc:hte,setupFunc:cte},mte=!0,gte=pa(Li,mte),yte=Bt(Bl);function P3(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var Zv;function xte(e){Zv=e.wasm.cwrap(Wi,"number",["number","number","number","number","number"])}function Ate(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=Zv(u,p,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=P3(t,c);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var bte={kernelName:Wi,backendName:"wasm",setupFunc:xte,kernelFunc:Ate},Yv;function vte(e){Yv=e.wasm.cwrap(Wl,"number",["number","number","number","number","number","bool"])}function kte(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Yv(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=P3(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var wte={kernelName:Wl,backendName:"wasm",setupFunc:vte,kernelFunc:kte},Jv;function Ite(e){Jv=e.wasm.cwrap(Vi,"number",["number","number","number","number","number","number"])}function Ste(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Jv(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=P3(t,d);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[y,x]}var Tte={kernelName:Vi,backendName:"wasm",setupFunc:Ite,kernelFunc:Ste},Cte=!1,Nte=pa(Bi,Cte,"bool"),Qv;function Ete(e){Qv=e.wasm.cwrap(Ui,null,["number","number","number","number","number"])}function Rte(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),p=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return Qv(c,i,o,l,p),u}var Mte={kernelName:Ui,backendName:"wasm",setupFunc:Ete,kernelFunc:Rte};function $te(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var _te={kernelName:Vl,backendName:"wasm",kernelFunc:$te};function Pte(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return T2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching 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Int32Array(v.computeStrides(g)).buffer),A=t.makeOutput(g,r.dtype),b=t.dataIdMap.get(A.dataId).id,k=t.dataIdMap.get(r.dataId).id,S=t.dataIdMap.get(s.dataId).id,C=i==="nearest"?1:2,N;switch(o){case"constant":N=1;break;case"reflect":N=2;break;case"wrap":N=3;break;case"nearest":N=4;break;default:N=1;break}return w8(k,S,s.shape[0]>1,p,f,m,h,d,c,y,r.shape.length-1,x,g.length-1,C,N,l,b),A}var yne={kernelName:mo,backendName:"wasm",setupFunc:mne,kernelFunc:gne};function xne(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),u=0;for(let h=0;h<o;h++)h!==s&&(l[u++]=r.shape[h]);let p=new Array(i),c=new Array(o).fill(0),d=r.shape.slice();d[s]=1;for(let h=0;h<p.length;h++)c[s]=h,p[h]=qs({inputs:{x:r},attrs:{begin:c,size:d},backend:a});return p.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var Ane={kernelName:au,backendName:"wasm",kernelFunc:xne};function bne(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(0),n}var vne={kernelName:nu,backendName:"wasm",kernelFunc:bne},kne=[XJ,KJ,YJ,eQ,lQ,pQ,fQ,yQ,vQ,CQ,NQ,EQ,$Q,_Q,OQ,LQ,BQ,WQ,GQ,qQ,ZQ,QQ,aee,nee,see,iee,oee,lee,pee,cee,fee,yee,bee,wee,Tee,Eee,Mee,_ee,tQ,Pee,Dee,Lee,Wee,Vee,Gee,Hee,qee,Kee,Jee,ete,nte,ite,ute,pte,fte,gte,yte,bte,wte,Tte,Nte,Mte,_te,Fte,a8,Lte,Vte,Hte,qte,Kte,Zte,Yte,Jte,xQ,tae,rae,oae,dae,pae,cae,mae,xae,vae,kae,SQ,Sae,Cae,Rae,_ae,Fae,Dae,Lae,Bae,Wae,Uae,jae,Kae,Yae,Qae,tne,nne,ine,one,lne,pne,fne,yne,sQ,Ane,vne];for(let e of kne)gn(e);var N2=V();N2.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return 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]))}catch(e){return!1}});N2.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(N2.get("IS_NODE"))return!1;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]))}catch(e){return!1}});var Py=xl(aS()),wne=xl(nS()),Fy=xl(rS()),Oy=Py.default||Py,Ine=Fy.default||Fy,I8=class extends Al{constructor(e){super(),this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(S8),E2=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new wd(this,vt())}write(e,t,a){let n={id:this.dataIdNextNumber++};return this.move(n,e,t,a,1),n}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,a,n,r){let s=this.dataIdNextNumber++;if(n==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:a,dtype:n,memoryOffset:null,refCount:r});return}let i=v.sizeFromShape(a),o=i*v.bytesPerElement(n),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:a,dtype:n,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e,t,a){let{memoryOffset:n,dtype:r,shape:s,stringBytes:i}=this.dataIdMap.get(e);if(r==="string")return(t==null||t===0)&&(a==null||a>=i.length)?i:i.slice(t,a);t=t||0,a=a||v.sizeFromShape(s);let o=v.bytesPerElement(r),l=this.wasm.HEAPU8.slice(n+t*o,n+a*o);return Cne(l.buffer,r)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let a=this.dataIdMap.get(e);if(a.refCount--,!t&&a.refCount>0)return!1;this.wasm._free(a.memoryOffset),this.wasm.tfjs.disposeData(a.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,a){let n;if(a==null)n=this.write(null,e,t);else{let r=this.dataIdNextNumber++;n={id:r},this.dataIdMap.set(n,{id:r,memoryOffset:a,shape:e,dtype:t,refCount:1});let s=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,a)}return{dataId:n,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:a}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(a),s=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(n,r,s);case"int32":return new Int32Array(n,r,s);case"bool":return new Uint8Array(n,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function Sne(e){return(t,a)=>(v.fetch(e,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at 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A=Array.from(t.readSync(r.dataId)),b=Array.from(t.readSync(h.dataId));x=T.getSparseReshapeInputOutputMismatchErrorMessage(A,b);break}default:x=""}if(t.disposeData(m.dataId),x)throw t.disposeData(c.dataId),t.disposeData(h.dataId),new Error(x);return[c,h]}var _ae={kernelName:eu,backendName:"wasm",setupFunc:Mae,kernelFunc:$ae},f8;function m8(e){f8=e.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function g8(e,t){let{backend:a,inputs:n}=e,{data:r,indices:s,segmentIds:i}=n,o=s.shape[0],l=a.readSync(i.dataId,o-1,o)[0],u=o>0?l+1:0;if(u<0)throw new Error(T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=r.shape.slice();p[0]=u;let c=a.dataIdMap.get(r.dataId).id,d=a.dataIdMap.get(s.dataId).id,h=a.dataIdMap.get(i.dataId).id,f=a.makeOutput(p,r.dtype),m=a.dataIdMap.get(f.dataId).id,g=a.makeOutput([4],"int32"),y=a.dataIdMap.get(g.dataId).id;f8(c,Ct[r.dtype],r.shape[0],d,h,m,y,t,0);let x=a.readSync(g.dataId),A;switch(x[0]){case 0:{A=T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{A=T.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:A=T.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(x[1],x[2]);break;case 3:A=T.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(x[1],x[2],x[3]);break;default:A=""}if(a.disposeData(g.dataId),A)throw a.disposeData(f.dataId),new Error(A);return f}function Pae(e){return g8(e,!0)}var Fae={kernelName:Bd,backendName:"wasm",setupFunc:m8,kernelFunc:Pae};function Oae(e){return g8(e,!1)}var Dae={kernelName:Wd,backendName:"wasm",setupFunc:m8,kernelFunc:Oae};function zae(e){let{inputs:t,attrs:a,backend:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=new Array(r.shape.length).fill(0),p=r.shape.slice();return l.map(c=>{let d=[...p];d[o]=c;let h=qs({inputs:{x:r},attrs:{begin:u,size:d},backend:n});return u[o]+=c,h})}var Lae={kernelName:Ql,backendName:"wasm",kernelFunc:zae},Bae=Bt(so),Wae=Bt(Ud),Vae=!0,Uae=pa(lo,Vae),y8;function Gae(e){y8=e.wasm.cwrap(ss,null,["number","number","number","number"])}function Hae(e){let{backend:t,inputs:a,attrs:n}=e,{alpha:r}=n,{x:s}=a,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return y8(i,r,Ct[s.dtype],l),o}var jae={kernelName:ss,backendName:"wasm",setupFunc:Gae,kernelFunc:Hae},x8;function qae(e){x8=e.wasm.cwrap(uo,null,["number","array","number","array","array","array","array","array","number","number"])}function Xae(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=St.sliceInfo(r.shape,s,i,o,l,u,p,c,d),k;if(m)k=La({inputs:{x:r},backend:t,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, 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ene(e){let{backend:t,inputs:a,attrs:n}=e,{input:r}=a,{numBuckets:s}=n,i=t.readSync(r.dataId),o=m3(i,s),l=t.makeOutput(r.shape,"int32");return t.typedArrayFromHeap(l).set(o),l}var tne={kernelName:Hd,backendName:"wasm",kernelFunc:ene},ane=!0,nne=pa(po,ane),A8;function rne(e){A8=e.wasm.cwrap(io,null,["number","number","number","number"])}function sne(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=ds(i,r,t),f=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,f=T.getInnerMostAxes(f.length,u.shape.length))}T.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[m,g]=T.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(g),x=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;A8(l,y,Ct[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=T.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var ine={kernelName:io,backendName:"wasm",setupFunc:rne,kernelFunc:sne},one=Bt(co),lne=Bt(ho),b8;function une(e){b8=e.wasm.cwrap(rs,null,["number","array","number","array","number","number"])}function dne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,s=a.dataIdMap.get(r.dataId).id,{reps:i}=n,o=new Array(r.shape.length);for(let d=0;d<o.length;d++)o[d]=r.shape[d]*i[d];let l=new Uint8Array(new Int32Array(r.shape).buffer),u=new Uint8Array(new Int32Array(o).buffer),p=a.makeOutput(o,r.dtype),c=a.dataIdMap.get(p.dataId).id;return b8(s,l,r.shape.length,u,o.length,Ct[p.dtype],c),p}var pne={kernelName:rs,backendName:"wasm",setupFunc:une,kernelFunc:dne},v8;function cne(e){v8=e.wasm.cwrap(fo,null,["number","array","number","number","number","bool","number","number"])}var hne=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{k:r,sorted:s}=a,i=t.dataIdMap.get(n.dataId).id,o=new Uint8Array(new Int32Array(n.shape).buffer),l=n.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,n.dtype),p=t.dataIdMap.get(u.dataId).id,c=t.makeOutput(l,"int32"),d=t.dataIdMap.get(c.dataId).id;return v8(i,o,n.shape.length,Ct[n.dtype],r,s,p,d),[u,c]},fne={kernelName:fo,backendName:"wasm",setupFunc:cne,kernelFunc:hne},k8;function mne(e){k8=e.wasm.cwrap(mo,null,["number","number","bool","number","number","number","number","number","number","array","number","array","number","number","number","number","number"])}function gne(e){let{backend:t,inputs:a,attrs:n}=e,{image:r,transforms:s}=a,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,c,d,h]=r.shape,[f,m]=u!=null?u:[c,d],g=[p,f,m,h],y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(g)).buffer),A=t.makeOutput(g,r.dtype),b=t.dataIdMap.get(A.dataId).id,k=t.dataIdMap.get(r.dataId).id,S=t.dataIdMap.get(s.dataId).id,C=i==="nearest"?1:2,N;switch(o){case"constant":N=1;break;case"reflect":N=2;break;case"wrap":N=3;break;case"nearest":N=4;break;default:N=1;break}return k8(k,S,s.shape[0]>1,p,f,m,h,d,c,y,r.shape.length-1,x,g.length-1,C,N,l,b),A}var yne={kernelName:mo,backendName:"wasm",setupFunc:mne,kernelFunc:gne};function xne(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),u=0;for(let h=0;h<o;h++)h!==s&&(l[u++]=r.shape[h]);let p=new Array(i),c=new Array(o).fill(0),d=r.shape.slice();d[s]=1;for(let h=0;h<p.length;h++)c[s]=h,p[h]=qs({inputs:{x:r},attrs:{begin:c,size:d},backend:a});return p.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var Ane={kernelName:au,backendName:"wasm",kernelFunc:xne};function bne(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(0),n}var vne={kernelName:nu,backendName:"wasm",kernelFunc:bne},kne=[XJ,KJ,YJ,eQ,lQ,pQ,fQ,yQ,vQ,CQ,NQ,EQ,$Q,_Q,OQ,LQ,BQ,WQ,GQ,qQ,ZQ,QQ,aee,nee,see,iee,oee,lee,pee,cee,fee,yee,bee,wee,Tee,Eee,Mee,_ee,tQ,Pee,Dee,Lee,Wee,Vee,Gee,Hee,qee,Kee,Jee,ete,nte,ite,ute,pte,fte,gte,yte,bte,wte,Tte,Nte,Mte,_te,Fte,t8,Lte,Vte,Hte,qte,Kte,Zte,Yte,Jte,xQ,tae,rae,oae,dae,pae,cae,mae,xae,vae,kae,SQ,Sae,Cae,Rae,_ae,Fae,Dae,Lae,Bae,Wae,Uae,jae,Kae,Yae,Qae,tne,nne,ine,one,lne,pne,fne,yne,sQ,Ane,vne];for(let e of kne)gn(e);var N2=V();N2.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return 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]))}catch(e){return!1}});N2.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(N2.get("IS_NODE"))return!1;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]))}catch(e){return!1}});var _y=xl(aS()),wne=xl(nS()),Py=xl(rS()),Fy=_y.default||_y,Ine=Py.default||Py,w8=class extends Al{constructor(e){super(),this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(I8),E2=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new kd(this,vt())}write(e,t,a){let n={id:this.dataIdNextNumber++};return this.move(n,e,t,a,1),n}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,a,n,r){let s=this.dataIdNextNumber++;if(n==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:a,dtype:n,memoryOffset:null,refCount:r});return}let i=v.sizeFromShape(a),o=i*v.bytesPerElement(n),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:a,dtype:n,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e,t,a){let{memoryOffset:n,dtype:r,shape:s,stringBytes:i}=this.dataIdMap.get(e);if(r==="string")return(t==null||t===0)&&(a==null||a>=i.length)?i:i.slice(t,a);t=t||0,a=a||v.sizeFromShape(s);let o=v.bytesPerElement(r),l=this.wasm.HEAPU8.slice(n+t*o,n+a*o);return Cne(l.buffer,r)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let a=this.dataIdMap.get(e);if(a.refCount--,!t&&a.refCount>0)return!1;this.wasm._free(a.memoryOffset),this.wasm.tfjs.disposeData(a.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,a){let n;if(a==null)n=this.write(null,e,t);else{let r=this.dataIdNextNumber++;n={id:r},this.dataIdMap.set(n,{id:r,memoryOffset:a,shape:e,dtype:t,refCount:1});let s=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,a)}return{dataId:n,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:a}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(a),s=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(n,r,s);case"int32":return new Int32Array(n,r,s);case"bool":return new Uint8Array(n,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function Sne(e){return(t,a)=>(v.fetch(e,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${e}'`),n.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{a(s.instance,s.module)})})}),{})}function Oy(e,t,a){if(Vc!=null)return Vc;let n="tfjs-backend-wasm.wasm";return e&&t?n="tfjs-backend-wasm-threaded-simd.wasm":e&&(n="tfjs-backend-wasm-simd.wasm"),Qu!=null&&Qu[n]!=null?Qu[n]:a+n}async function Tne(){let[e,t]=await Promise.all([V().getAsync("WASM_HAS_SIMD_SUPPORT"),V().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((a,n)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=wne.wasmWorkerContents.replace(/\n/g,"\\n"),p=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(p)}return o.endsWith(".wasm")?Oy(e,t,Zu!=null?Zu:l):l+o},F3&&(r.instantiateWasm=Sne(Oy(e,t,Zu!=null?Zu:"")));let s=!1;r.onAbort=()=>{s||ed||(ed=!0,n({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()`");Vc=e,F3=t}function Fh(e,t=!1){if(ed)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")Zu=e;else{Qu=e;let a=Nne.filter(n=>Qu[n]==null);if(a.length>0)throw new Error(`There were no entries found for the following binaries: ${a.join(",")}. 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           {
             var oldValue = 0;
             loop {
@@ -4886,7 +4886,7 @@ return a / b;`,jK=`
         fn main()
       `;break;case 1:t=`
         fn main(${e[0]} : i32)
-      `;break;default:throw Error("Unreachable")}return t}function Wy(e,t){let a;return a=`
+      `;break;default:throw Error("Unreachable")}return t}function By(e,t){let a;return a=`
      ${Lne(t)}
       fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
                 @builtin(global_invocation_id) GlobalId : vec3<u32>,
@@ -4912,7 +4912,7 @@ return a / b;`,jK=`
 
       // Only used when the y/z dimension of workgroup size is 1.
       fn getGlobalIndex() -> i32 {
-        ${T8(a)?"  return i32(globalId.x);":`  return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +
+        ${S8(a)?"  return i32(globalId.x);":`  return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +
                 workgroupId.y * numWorkgroups.x + workgroupId.x) * ${r}u +
                 localIndex);
         `}
@@ -4924,23 +4924,23 @@ return a / b;`,jK=`
           outShapeStrides : vec2<i32>,
         };
 
-        @group(0) @binding(0) var<storage, read_write> result: array<${ad(t.dtype,a.isVec4)}>;
+        @group(0) @binding(0) var<storage, read_write> result: array<${td(t.dtype,a.isVec4)}>;
         @group(0) @binding(2) var<uniform> uniforms: Uniform;
-      `);let h=Gy(a);return[Vy,n.join(`
-`),Uy(t.shape),a.getUserCode(),Wy(h,a)].join(`
+      `);let h=Uy(a);return[Wy,n.join(`
+`),Vy(t.shape),a.getUserCode(),By(h,a)].join(`
 `)}let s="struct Uniforms { NAN : f32, INFINITY : f32, ";a.variableNames.forEach((h,f)=>{let m=oa(e[f].shape.length);s+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${m}, `});let i=oa(t.shape.length);s+=`outShape : ${i}, `;let o=t.shape.length-1,l=oa(o);s+=`
          outShapeStrides: ${l}, `,a.size&&(s+="size : i32, "),a.uniforms&&(s+=a.uniforms),s+="};",s=Kne(s),n.push(s),a.atomic?n.push(`
       @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
     `):n.push(`
-      @group(0) @binding(0) var<storage, read_write> result: array<${ad(t.dtype,a.isVec4)}>;
+      @group(0) @binding(0) var<storage, read_write> result: array<${td(t.dtype,a.isVec4)}>;
     `),a.variableNames.forEach((h,f)=>{n.push(`
-      @group(0) @binding(${1+f}) var<storage, read> ${h}: array<${a.variableTypes?a.variableTypes[f]:ad(e[f].dtype,a.isVec4)}>;
+      @group(0) @binding(${1+f}) var<storage, read> ${h}: array<${a.variableTypes?a.variableTypes[f]:td(e[f].dtype,a.isVec4)}>;
         `)}),s!==""&&n.push(`
       @group(0) @binding(${1+a.variableNames.length}) var<uniform> uniforms: Uniforms;
-      `);let u=jne(t.shape,a.dispatchLayout),p=[Vy,n.join(`
-`)+Vne,Uy(t.shape),u,qne(t.shape.length)];a.atomic||p.push(Xne(t.shape,t.dtype,a.isVec4));let c=e.map((h,f)=>Hne(h,t.shape,a.variableTypes?a.variableTypes[f]==="vec4<f32>":a.isVec4,a.dispatchLayout.x.length===t.shape.length)).join(`
-`);p.push(c),p.push(a.getUserCode());let d=Gy(a);return p.push(Wy(d,a)),p.join(`
-`)}function Wne(e,t,a,n){let r=e.shaderKey;if(e.isFromPixels)return r;let s=a.map(p=>p.dtype).concat(n.dtype),i=a.map(p=>T.getBroadcastDims(p.shape,n.shape)),o=a.map(p=>v.arraysEqual(p.shape,n.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=T8(e)?"flatDispatch":"";return r+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+t.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,r}var Vy=`
+      `);let u=jne(t.shape,a.dispatchLayout),p=[Wy,n.join(`
+`)+Vne,Vy(t.shape),u,qne(t.shape.length)];a.atomic||p.push(Xne(t.shape,t.dtype,a.isVec4));let c=e.map((h,f)=>Hne(h,t.shape,a.variableTypes?a.variableTypes[f]==="vec4<f32>":a.isVec4,a.dispatchLayout.x.length===t.shape.length)).join(`
+`);p.push(c),p.push(a.getUserCode());let d=Uy(a);return p.push(By(d,a)),p.join(`
+`)}function Wne(e,t,a,n){let r=e.shaderKey;if(e.isFromPixels)return r;let s=a.map(p=>p.dtype).concat(n.dtype),i=a.map(p=>T.getBroadcastDims(p.shape,n.shape)),o=a.map(p=>v.arraysEqual(p.shape,n.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=S8(e)?"flatDispatch":"";return r+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+t.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,r}var Wy=`
   struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
   struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
 
@@ -5003,7 +5003,7 @@ return a / b;`,jK=`
   fn isinf(val: f32) -> bool {
     return abs(val) == uniforms.INFINITY;
   }
-`;function Uy(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let a=v.computeStrides(e),n=oa(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(a.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+`;function Vy(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let a=v.computeStrides(e),n=oa(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(a.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;return s="var index2 = index;"+a.map((i,o)=>{let l=`let ${r[o]} = index2 / uniforms.outShapeStrides.${br(o)}`,u=o===a.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides.${br(o)}`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides.${br(o)}`;return`${l}; ${u};`}).join(""),`
@@ -5126,7 +5126,7 @@ return a / b;`,jK=`
               coords.u * uniforms.outShapeStrides.u +
               coords.v;
         }
-        `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function T8(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function ad(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Xne(e,t,a){let n=e.length,r=ad(t,a),s;if(a?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function S8(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function td(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Xne(e,t,a){let n=e.length,r=td(t,a),s;if(a?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${r}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -5154,10 +5154,10 @@ return a / b;`,jK=`
         let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
         setOutputAtIndexI32(flatIndex, value);
       }
-    `}return s}function Kne(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function Gy(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var C8={};Ze(C8,{GPUBytesPerElement:()=>R2,MatMulProgramType:()=>Pn,assertNotComplex:()=>E8,computeDispatch:()=>we,computeWorkPerThreadForConv2d:()=>z3,computeWorkgroupInfoForMatMul:()=>N8,computeWorkgroupSizeForConv2d:()=>D3,flatDispatchLayout:()=>$e,isWebGPUSupported:()=>L3,tilesFitEvenlyIntoShape:()=>Zne});var Ds=e=>{let t=1;for(let a=0;a<e.length;a++)t*=e[a];return t};function Zne(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((a,n)=>a%e[n]===0)}function we(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(Ds(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(Ds(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(Ds(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function N8(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function D3(e,t,a=!1){if(a)return[8,8,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function z3(e,t,a=!1){if(a)return[4,4,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function $e(e){return{x:e.map((t,a)=>a)}}function R2(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function L3(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function E8(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Pn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Pn||(Pn={}));var Yne=V().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Jne=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Dh=class extends Al{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!L3())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new Pne(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Fne(this.device),this.textureManager=new One(this.device),this.tensorMap=new wd(this,vt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),V().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 Dh.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let a=this.tensorMap.get(e);if(this.decRef(e),!t&&a.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if("texture"in t.resourceInfo){let a=t.resourceInfo;a.texture instanceof GPUTexture&&this.textureManager.releaseTexture(a.texture,a.width,a.height,a.format,a.usage),a.texture=null}else{let a=t.resourceInfo;this.bufferManager.releaseBuffer(a.buffer,a.size,a.usage),a.buffer=null}t.resourceInfo=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,a){if(a==="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.tensorMap.set(n,{dtype:a,shape:t,values:e,refCount:1}),n}move(e,t,a,n,r){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:a,values:t,refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,a,0,t),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=a.getMappedRange().slice(0);return a.unmap(),a!=null&&this.bufferManager.releaseBuffer(a,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),V().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let a=this.tensorMap.get(e);return this.releaseResource(e),a.values=t,a.values}readSync(e){let t=this.tensorMap.get(e),{values:a}=t;if(a==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return a}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:a}=t;if(a!=null)return this.convertAndCacheOnCPU(e,a);let n;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=r[0],i=r[1];n=T.mergeRealAndImagArrays(s,i)}else{let r=t.resourceInfo,s=await this.getBufferData(r.buffer,r.size);n=v.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,a){let n=this.bufferManager.acquireBuffer(t,a);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,a){let n=e.buffer;if(a==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let r={id:this.nextDataId()};this.tensorMap.set(r,{dtype:a,shape:t,values:null,refCount:1,external:e.zeroCopy});let s=this.tensorMap.get(r),i=R2(s.dtype)*v.sizeFromShape(s.shape);if(e.buffer.size<i)throw new Error(`GPUBuffer size(${e.buffer.size}) is smaller than tensor size(${i})!`);if((e.buffer.usage&(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))!==(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))throw new Error("GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!");return e.zeroCopy!==!0&&(n=this.copyBuffer(n,i,n.usage)),s.resourceInfo={size:n.size,usage:n.usage,buffer:n},vt().makeTensorFromDataId(r,t,a,this)}readToGPU(e){let t=this.tensorMap.get(e),{values:a,dtype:n,shape:r,resourceInfo:s}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(s==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=s.size,o=this.bufferManager.acquireBuffer(i,s.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s.buffer,0,o,0,i),this.submitQueue();let l=this.makeTensorInfo(r,n),u=vt().makeTensorFromTensorInfo(l),p=this.tensorMap.get(l.dataId);return p.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:o},{tensorRef:u,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return _e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.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(r);return i.kernelMs=v.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}makeTensorInfo(e,t,a){return t==="string"&&a!=null&&a.length>0&&v.isString(a[0])&&(a=a.map(n=>v.encodeString(n))),{dataId:this.write(a,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let a=t.resourceInfo;return{offset:0,size:a.size,buffer:a.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let a=R2(t.dtype)*v.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(a,this.defaultGpuBufferUsage());if(t.resourceInfo={size:a,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let r=this.bufferManager.acquireUploadBuffer(a,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,n,0,a);let i={size:a,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,a=0,n=[],r=1;e.forEach(l=>{l.data.length===0&&(l.data=[1]);let u;switch(l.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:v.assert(!1,()=>`Unsupported ${l.data.length}D shape`)}(a===5||a===6)&&(u=16),u>r&&(r=u),t=Math.ceil(t/u)*u,a=l.data.length,n.push(t),t+=l.data.length*4}),t=Math.ceil(t/r)*r;let s=new ArrayBuffer(t);e.forEach((l,u)=>{let p=n[u];l.type==="int32"?new Int32Array(s,p,l.data.length).set(l.data):l.type==="uint32"?new Uint32Array(s,p,l.data.length).set(l.data):new Float32Array(s,p,l.data.length).set(l.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,s,0,t);let o={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(o),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,a,n,r){if(r||(r=this.makeTensorInfo(e.outputShape,a)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=Jne(this.device,e);let s=[],i=[];if(!e.isFromPixels){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(r).map(g=>g.shape);let f="int32";i.map(g=>{s.push({type:f,data:g})});let m=v.computeStrides(r.shape);if(s.push({type:f,data:m}),e.size){let g=v.sizeFromShape(e.outputShape);s.push({type:f,data:[e.isVec4?g/4:g]})}}let o=t.map((f,m)=>{if(f.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(f.dataId),{dtype:this.tensorMap.get(f.dataId).dtype,shape:f.shape,name:e.variableNames[m]}}),l=Wne(e,i,o,r),u;l in this.pipelineCache?u=this.pipelineCache[l]:(u=zne(this.device,e,o,r),this.pipelineCache[l]=u),n&&(s=[...s,...n]);let p=[this.tensorToBinding(r),...t.map(f=>this.tensorToBinding(f)),this.makeUniforms(s)],c=this.device.createBindGroup({layout:u.getBindGroupLayout(0),entries:p.map((f,m)=>({binding:m,resource:f}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),h=this.activeTimers!=null;return h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(u),d.setBindGroup(0,c),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(f=>{this.commandQueueOwnedIds.add(f.dataId)}),this.commandQueueOwnedIds.add(r.dataId),V().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),h&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a=this.bufferManager.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,a,0,16),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(a.getMappedRange()),r=Number(n[1]-n[0]);return a.unmap(),this.bufferManager.releaseBuffer(a,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Yne){return V().getBool("WEBGPU_CPU_FORWARD")&&e.every(a=>this.tensorMap.get(a.dataId).resourceInfo==null&&v.sizeFromShape(a.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDataPendingDisposal.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}};Dh.nextDataId=0;L3()&&yo("webgpu",async()=>{V().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:V().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={};t.features.has("timestamp-query-inside-passes")&&(a.requiredFeatures=["timestamp-query-inside-passes"]);let n=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize};let r=await t.requestDevice(a),s=await t.requestAdapterInfo();return new Dh(r,s)},3);var Pe;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.EQUAL=5]="EQUAL",e[e.GREATER=6]="GREATER",e[e.GREATER_EQUAL=7]="GREATER_EQUAL",e[e.INT_DIV=8]="INT_DIV",e[e.LESS=9]="LESS",e[e.LESS_EQUAL=10]="LESS_EQUAL",e[e.LOGICAL_AND=11]="LOGICAL_AND",e[e.LOGICAL_OR=12]="LOGICAL_OR",e[e.MAX=13]="MAX",e[e.MIN=14]="MIN",e[e.MOD=15]="MOD",e[e.MUL=16]="MUL",e[e.NOT_EQUAL=17]="NOT_EQUAL",e[e.POW=18]="POW",e[e.PRELU=19]="PRELU",e[e.SQUARED_DIFFERENCE=20]="SQUARED_DIFFERENCE",e[e.SUB=21]="SUB"})(Pe||(Pe={}));var R8=`
+    `}return s}function Kne(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function Uy(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var T8={};Ze(T8,{GPUBytesPerElement:()=>R2,MatMulProgramType:()=>Pn,assertNotComplex:()=>N8,computeDispatch:()=>we,computeWorkPerThreadForConv2d:()=>z3,computeWorkgroupInfoForMatMul:()=>C8,computeWorkgroupSizeForConv2d:()=>D3,flatDispatchLayout:()=>$e,isWebGPUSupported:()=>L3,tilesFitEvenlyIntoShape:()=>Zne});var Ds=e=>{let t=1;for(let a=0;a<e.length;a++)t*=e[a];return t};function Zne(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((a,n)=>a%e[n]===0)}function we(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(Ds(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(Ds(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(Ds(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function C8(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function D3(e,t,a=!1){if(a)return[8,8,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function z3(e,t,a=!1){if(a)return[4,4,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function $e(e){return{x:e.map((t,a)=>a)}}function R2(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function L3(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function N8(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Pn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Pn||(Pn={}));var Yne=V().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Jne=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Oh=class extends Al{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!L3())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new Pne(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Fne(this.device),this.textureManager=new One(this.device),this.tensorMap=new kd(this,vt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),V().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 Oh.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let a=this.tensorMap.get(e);if(this.decRef(e),!t&&a.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(t.external){t.resourceInfo=null;return}if("texture"in t.resourceInfo){let a=t.resourceInfo;a.texture instanceof GPUTexture&&this.textureManager.releaseTexture(a.texture,a.width,a.height,a.format,a.usage),a.texture=null}else{let a=t.resourceInfo;this.bufferManager.releaseBuffer(a.buffer,a.size,a.usage),a.buffer=null}t.resourceInfo=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,a){if(a==="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.tensorMap.set(n,{dtype:a,shape:t,values:e,refCount:1}),n}move(e,t,a,n,r){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:a,values:t,refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,a,0,t),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=a.getMappedRange().slice(0);return a.unmap(),a!=null&&this.bufferManager.releaseBuffer(a,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),V().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let a=this.tensorMap.get(e);return this.releaseResource(e),a.values=t,a.values}readSync(e){let t=this.tensorMap.get(e),{values:a}=t;if(a==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return a}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:a}=t;if(a!=null)return this.convertAndCacheOnCPU(e,a);let n;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=r[0],i=r[1];n=T.mergeRealAndImagArrays(s,i)}else{let r=t.resourceInfo,s=await this.getBufferData(r.buffer,r.size);n=v.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,a){let n=this.bufferManager.acquireBuffer(t,a);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,a){let n=e.buffer;if(a==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let r={id:this.nextDataId()};this.tensorMap.set(r,{dtype:a,shape:t,values:null,refCount:1,external:e.zeroCopy});let s=this.tensorMap.get(r),i=R2(s.dtype)*v.sizeFromShape(s.shape);if(e.buffer.size<i)throw new Error(`GPUBuffer size(${e.buffer.size}) is smaller than tensor size(${i})!`);if((e.buffer.usage&(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))!==(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))throw new Error("GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!");return e.zeroCopy!==!0&&(n=this.copyBuffer(n,i,n.usage)),s.resourceInfo={size:n.size,usage:n.usage,buffer:n},vt().makeTensorFromDataId(r,t,a,this)}readToGPU(e){let t=this.tensorMap.get(e),{values:a,dtype:n,shape:r,resourceInfo:s}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(s==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=s.size,o=this.bufferManager.acquireBuffer(i,s.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s.buffer,0,o,0,i),this.submitQueue();let l=this.makeTensorInfo(r,n),u=vt().makeTensorFromTensorInfo(l),p=this.tensorMap.get(l.dataId);return p.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:o},{tensorRef:u,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return _e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.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(r);return i.kernelMs=v.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}makeTensorInfo(e,t,a){return t==="string"&&a!=null&&a.length>0&&v.isString(a[0])&&(a=a.map(n=>v.encodeString(n))),{dataId:this.write(a,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let a=t.resourceInfo;return{offset:0,size:a.size,buffer:a.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let a=R2(t.dtype)*v.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(a,this.defaultGpuBufferUsage());if(t.resourceInfo={size:a,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let r=this.bufferManager.acquireUploadBuffer(a,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,n,0,a);let i={size:a,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,a=0,n=[],r=1;e.forEach(l=>{l.data.length===0&&(l.data=[1]);let u;switch(l.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:v.assert(!1,()=>`Unsupported ${l.data.length}D shape`)}(a===5||a===6)&&(u=16),u>r&&(r=u),t=Math.ceil(t/u)*u,a=l.data.length,n.push(t),t+=l.data.length*4}),t=Math.ceil(t/r)*r;let s=new ArrayBuffer(t);e.forEach((l,u)=>{let p=n[u];l.type==="int32"?new Int32Array(s,p,l.data.length).set(l.data):l.type==="uint32"?new Uint32Array(s,p,l.data.length).set(l.data):new Float32Array(s,p,l.data.length).set(l.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,s,0,t);let o={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(o),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,a,n,r){if(r||(r=this.makeTensorInfo(e.outputShape,a)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=Jne(this.device,e);let s=[],i=[];if(!e.isFromPixels){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(r).map(g=>g.shape);let f="int32";i.map(g=>{s.push({type:f,data:g})});let m=v.computeStrides(r.shape);if(s.push({type:f,data:m}),e.size){let g=v.sizeFromShape(e.outputShape);s.push({type:f,data:[e.isVec4?g/4:g]})}}let o=t.map((f,m)=>{if(f.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(f.dataId),{dtype:this.tensorMap.get(f.dataId).dtype,shape:f.shape,name:e.variableNames[m]}}),l=Wne(e,i,o,r),u;l in this.pipelineCache?u=this.pipelineCache[l]:(u=zne(this.device,e,o,r),this.pipelineCache[l]=u),n&&(s=[...s,...n]);let p=[this.tensorToBinding(r),...t.map(f=>this.tensorToBinding(f)),this.makeUniforms(s)],c=this.device.createBindGroup({layout:u.getBindGroupLayout(0),entries:p.map((f,m)=>({binding:m,resource:f}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),h=this.activeTimers!=null;return h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(u),d.setBindGroup(0,c),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(f=>{this.commandQueueOwnedIds.add(f.dataId)}),this.commandQueueOwnedIds.add(r.dataId),V().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),h&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a=this.bufferManager.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,a,0,16),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(a.getMappedRange()),r=Number(n[1]-n[0]);return a.unmap(),this.bufferManager.releaseBuffer(a,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Yne){return V().getBool("WEBGPU_CPU_FORWARD")&&e.every(a=>this.tensorMap.get(a.dataId).resourceInfo==null&&v.sizeFromShape(a.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDataPendingDisposal.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}};Oh.nextDataId=0;L3()&&yo("webgpu",async()=>{V().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:V().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={};t.features.has("timestamp-query-inside-passes")&&(a.requiredFeatures=["timestamp-query-inside-passes"]);let n=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize};let r=await t.requestDevice(a),s=await t.requestAdapterInfo();return new Oh(r,s)},3);var Pe;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.EQUAL=5]="EQUAL",e[e.GREATER=6]="GREATER",e[e.GREATER_EQUAL=7]="GREATER_EQUAL",e[e.INT_DIV=8]="INT_DIV",e[e.LESS=9]="LESS",e[e.LESS_EQUAL=10]="LESS_EQUAL",e[e.LOGICAL_AND=11]="LOGICAL_AND",e[e.LOGICAL_OR=12]="LOGICAL_OR",e[e.MAX=13]="MAX",e[e.MIN=14]="MIN",e[e.MOD=15]="MOD",e[e.MUL=16]="MUL",e[e.NOT_EQUAL=17]="NOT_EQUAL",e[e.POW=18]="POW",e[e.PRELU=19]="PRELU",e[e.SQUARED_DIFFERENCE=20]="SQUARED_DIFFERENCE",e[e.SUB=21]="SUB"})(Pe||(Pe={}));var E8=`
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
-  `,zh=`
+  `,Dh=`
   resultTemp = select(
       resultTemp, vec4<f32>(valueForNaN),
       vec4<bool>(isNaN) | isnanVec4(a) | isnanVec4(b));
@@ -5190,7 +5190,7 @@ return a / b;`,jK=`
 `,pre="return f32(a < b);",cre="return vec4<f32>(a < b);",hre="return f32(a <= b);",fre="return vec4<f32>(a <= b);",mre="return f32(a >= 1.0 && b >= 1.0);",gre=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
   vec4<f32>(b >= vec4<f32>(1.0)));`,yre="return f32(a >= 1.0 || b >= 1.0);",xre=`return min(vec4<f32>(a >= vec4<f32>(1.0)) +
   vec4<f32>(b >= vec4<f32>(1.0)), vec4<f32>(1.0));`,Are=`
-  ${R8}
+  ${E8}
   if (b == 0.) {
     return uniforms.NAN;
   }
@@ -5204,7 +5204,7 @@ return a / b;`,jK=`
   let isNaN = !vec4<bool>(b);
   let valueForNaN = uniforms.NAN;
   var resultTemp = vec4<f32>(a % b);
-  ${zh}
+  ${Dh}
 
   if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {
     resultTemp[0] = (resultTemp[0] + b[0]) % b[0];
@@ -5228,7 +5228,7 @@ return a / b;`,jK=`
 `,wre=`
   var resultTemp = vec4<f32>(a != b);
   let valueForNaN = 1.0;
-  ${zh}
+  ${Dh}
 
   return resultTemp;
 `,Ire=`
@@ -5264,12 +5264,12 @@ return a / b;`,jK=`
   }
   let isNaN = (a < vec4<f32>(0.0)) & (floor(b) < b);
   let valueForNaN = uniforms.NAN;
-  ${zh}
+  ${Dh}
   return resultTemp;
 `,Tre="if (a < 0.0) { return b * a; }  return a;",Cre=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-`,Nre="return (a - b) * (a - b);",Ere="return a - b;";function Dm(e,t,a="uniforms.NAN"){let n=t?zh:R8;return t?`
+`,Nre="return (a - b) * (a - b);",Ere="return a - b;";function Dm(e,t,a="uniforms.NAN"){let n=t?Dh:E8;return t?`
     let valueForNaN = ${a};
     var resultTemp = vec4<f32>(${e}(a, b));
     `+n+`
@@ -5392,7 +5392,7 @@ return a / b;`,jK=`
       }`,i}function ko(e,t){return`
       ${e?"value = value + getBiasByOutputCoords(coords);":""}
       ${t?"value = activation(value, coords);":""}
-      `}function M8(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=`
+      `}function R8(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=`
       ${e?"value = getA(batch, col, row);":"value = getA(batch, row, col);"}
 
     `,o=t?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return`
@@ -5415,7 +5415,7 @@ return a / b;`,jK=`
     return value;
   }
   `}function W3(e,t,a,n,r=!1,s=!1,i=!1,o=1){return`
-  ${M8(a,n,r,s,i,o)}
+  ${R8(a,n,r,s,i,o)}
   fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${$t(o)}) {
     let col = colIn * ${o};
     ${r&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
@@ -5451,7 +5451,7 @@ return a / b;`,jK=`
           acc[i] = BCached1 * ACached.y + acc[i];
           acc[i] = BCached2 * ACached.z + acc[i];
           ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
-        }`;function Lh(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=t[1]*e[1],u=t[0]*e[0],p=a?l:n,c=a?n:l,d=p/t[0],h=n/t[1],f=e[1];return v.assert((a&&d===4&&e[1]===4||!a&&(d===3||d===4))&&p%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${d} and workPerThread[1] ${e[1]} must be 4.
+        }`;function zh(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=t[1]*e[1],u=t[0]*e[0],p=a?l:n,c=a?n:l,d=p/t[0],h=n/t[1],f=e[1];return v.assert((a&&d===4&&e[1]===4||!a&&(d===3||d===4))&&p%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${d} and workPerThread[1] ${e[1]} must be 4.
           Otherwise, innerElementSize ${d} must be 3 or 4.
       tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`),`
   var<workgroup> mm_Asub : array<array<vec${d}<f32>, ${p/d}>, ${c}>;
@@ -5509,7 +5509,7 @@ return a / b;`,jK=`
     for (var innerRow = 0; innerRow < ${f}; innerRow++) {
         mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
     }
-  }`}var Hy=e=>e?`
+  }`}var Gy=e=>e?`
         mm_Asub[inputRow][inputCol] = mm_readA(batchA,
           kStart + inputRow,
           globalRowStart + inputCol);
@@ -5517,7 +5517,7 @@ return a / b;`,jK=`
         mm_Asub[inputRow][inputCol] = mm_readA(batchA,
           globalRowStart + inputRow,
           kStart + inputCol);
-        `,wse=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Bh(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],f=n/t[1],m=e[1],g=e[0],y=i?`
+        `,wse=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Lh(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],f=n/t[1],m=e[1],g=e[0],y=i?`
       let localRow = i32(localId.y);
       let localCol = i32(localId.x);
       let globalRowStart = i32(workgroupId.y) * ${l};
@@ -5528,7 +5528,7 @@ return a / b;`,jK=`
         // Load one tile of A into local memory.
         for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${t[1]}) {
           for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${t[0]}) {
-            ${Hy(a)}
+            ${Gy(a)}
           }
         }
         // Load one tile of B into local memory.
@@ -5583,7 +5583,7 @@ return a / b;`,jK=`
       for (var innerCol = 0; innerCol < ${h}; innerCol++) {
         let inputRow = tileRowA + innerRow;
         let inputCol = tileColA + innerCol;
-        ${Hy(a)}
+        ${Gy(a)}
       }
     }
 
@@ -5694,10 +5694,10 @@ return a / b;`,jK=`
 
       mm_write(batch, globalRow, globalCol, acc);
     }
-  `}var Tse=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=N8(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return`
+  `}var Tse=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=C8(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return`
       ${Cr(this.activation,this.hasPreluActivationWeights,this.isVec4)}
       ${W3(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}
-      ${this.isVec4?Lh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA,!0):this.isVectorA?Sse(this.workgroupSize,this.transposeA):Bh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}
+      ${this.isVec4?zh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA,!0):this.isVectorA?Sse(this.workgroupSize,this.transposeA):Lh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}
     `}};function Cse(e){return`
     var<workgroup> sumValues : array<f32, ${e}>;
     ${ke()} {
@@ -5792,7 +5792,7 @@ return a / b;`,jK=`
       ${W3(this.addBias,this.activation,this.transposeA,this.transposeB)}
       ${Ese(this.workgroupSize)}
     `}},Mse=class{constructor(e,t,a=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(a&&this.outputShape[1]%4===0||!a&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=we(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=a,this.transposeB=n,this.shaderKey=`matMulSplitK_${a}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=this.isVec4?4:1;return`
-      ${M8(!1,this.transposeB,!1,!1,!1,e)}
+      ${R8(!1,this.transposeB,!1,!1,!1,e)}
       fn mm_write(batch: i32, row : i32, colIn : i32, value : ${$t(e)}) {
         let col = colIn * ${e};
         if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {
@@ -5805,7 +5805,7 @@ return a / b;`,jK=`
           }
         }
       }
-      ${this.isVec4?Lh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Bh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}
+      ${this.isVec4?zh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Lh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}
     `}},$se=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return`
     ${Cr(this.activation,this.hasPreluActivationWeights)}
     ${ke("index")} {
@@ -5822,7 +5822,7 @@ return a / b;`,jK=`
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function Nr(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new _se(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var Pse={kernelName:Pl,backendName:"webgpu",kernelFunc:Nr};function Ie(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Fse={kernelName:Hl,backendName:"webgpu",kernelFunc:Ie};function Wh({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[p-2]:t.shape[p-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),A=xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],k=n?[x,f,d]:[x,d,f],S=Ie({inputs:{x:e},backend:r,attrs:{shape:b}}),C=Ie({inputs:{x:t},backend:r,attrs:{shape:k}}),N=[S,C],$=Math.max(y,x),M=[S,C],R=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[c]}],I,_,D=[$,h,f],W=V().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(W<0){let U=V().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),G=U>0?U:r.thresholdToIncreaseWorkgroups,q=$*Math.ceil(h/32)*Math.ceil(f/32);q<=G||h<=8&&q<=G*2?$*h*f<=128?W=Pn.MatMulReduceProgram:$===1&&d>=2e3?W=Pn.MatMulSplitKProgram:W=Pn.MatMulSmallOutputSizeProgram:W=Pn.MatMulPackedProgram}switch(W){case Pn.MatMulReduceProgram:I=new Nse(D,a,n,s,l,i);break;case Pn.MatMulSplitKProgram:{if(_=Nr({backend:r,attrs:{shape:D,value:0,dtype:e.dtype}}),I=new Mse(D,d,a,n),s||l){_=r.runWebGPUProgram(I,M,e.dtype,R,_);let G=new $se(_.shape,s,l,i),q=null,H=[_];s&&H.push(s),i&&H.push(i),l==="leakyrelu"&&(q=[{type:"float32",data:[o]}],G.uniforms+=" alpha : f32,");let B=r.runWebGPUProgram(G,H,_.dtype,q);N.push(_);let Z=Ie({inputs:{x:B},backend:r,attrs:{shape:A}});N.push(B);for(let X of N)r.disposeData(X.dataId);return Z}break}case Pn.MatMulSmallOutputSizeProgram:I=new Rse(b,k,D,a,n,s,l,i);break;case Pn.MatMulPackedProgram:let U=r.adapterInfo.isIntel();I=new Tse(b,D,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${W}.`)}s&&M.push(s),i&&M.push(i),l==="leakyrelu"&&(R.push({type:"float32",data:[o]}),I.uniforms+=" alpha : f32,"),_=r.runWebGPUProgram(I,M,e.dtype,R,_);let P=Ie({inputs:{x:_},backend:r,attrs:{shape:A}});N.push(_);for(let U of N)r.disposeData(U.dataId);return P}function Ose(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Wh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var Dse={kernelName:jr,backendName:"webgpu",kernelFunc:Ose},jy=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+  `}};function Nr(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new _se(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var Pse={kernelName:Pl,backendName:"webgpu",kernelFunc:Nr};function Ie(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Fse={kernelName:Hl,backendName:"webgpu",kernelFunc:Ie};function Bh({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[p-2]:t.shape[p-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),A=xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],k=n?[x,f,d]:[x,d,f],S=Ie({inputs:{x:e},backend:r,attrs:{shape:b}}),C=Ie({inputs:{x:t},backend:r,attrs:{shape:k}}),N=[S,C],$=Math.max(y,x),M=[S,C],R=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[c]}],I,_,D=[$,h,f],W=V().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(W<0){let U=V().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),G=U>0?U:r.thresholdToIncreaseWorkgroups,q=$*Math.ceil(h/32)*Math.ceil(f/32);q<=G||h<=8&&q<=G*2?$*h*f<=128?W=Pn.MatMulReduceProgram:$===1&&d>=2e3?W=Pn.MatMulSplitKProgram:W=Pn.MatMulSmallOutputSizeProgram:W=Pn.MatMulPackedProgram}switch(W){case Pn.MatMulReduceProgram:I=new Nse(D,a,n,s,l,i);break;case Pn.MatMulSplitKProgram:{if(_=Nr({backend:r,attrs:{shape:D,value:0,dtype:e.dtype}}),I=new Mse(D,d,a,n),s||l){_=r.runWebGPUProgram(I,M,e.dtype,R,_);let G=new $se(_.shape,s,l,i),q=null,H=[_];s&&H.push(s),i&&H.push(i),l==="leakyrelu"&&(q=[{type:"float32",data:[o]}],G.uniforms+=" alpha : f32,");let B=r.runWebGPUProgram(G,H,_.dtype,q);N.push(_);let Z=Ie({inputs:{x:B},backend:r,attrs:{shape:A}});N.push(B);for(let X of N)r.disposeData(X.dataId);return Z}break}case Pn.MatMulSmallOutputSizeProgram:I=new Rse(b,k,D,a,n,s,l,i);break;case Pn.MatMulPackedProgram:let U=r.adapterInfo.isIntel();I=new Tse(b,D,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${W}.`)}s&&M.push(s),i&&M.push(i),l==="leakyrelu"&&(R.push({type:"float32",data:[o]}),I.uniforms+=" alpha : f32,"),_=r.runWebGPUProgram(I,M,e.dtype,R,_);let P=Ie({inputs:{x:_},backend:r,attrs:{shape:A}});N.push(_);for(let U of N)r.disposeData(U.dataId);return P}function Ose(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Bh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var Dse={kernelName:jr,backendName:"webgpu",kernelFunc:Ose},Hy=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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 {
         ${B3(this.op,!1)}
@@ -5872,7 +5872,7 @@ return a / b;`,jK=`
            setOutputAtIndex(index, binaryOperation(a, b));
          }
        }
-       `;return e}};function Ja(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var zse={kernelName:wi,backendName:"webgpu",kernelFunc:Ja};function wo(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=Ja({inputs:{x:n},backend:a}),l=Ja({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Lse={kernelName:Cd,backendName:"webgpu",kernelFunc:wo},xu=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return`
+       `;return e}};function Ja(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var zse={kernelName:wi,backendName:"webgpu",kernelFunc:Ja};function wo(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=Ja({inputs:{x:n},backend:a}),l=Ja({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Lse={kernelName:Td,backendName:"webgpu",kernelFunc:wo},xu=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
         ${$s(this.op,!1)}
       }
@@ -5882,7 +5882,7 @@ return a / b;`,jK=`
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function et({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new xu(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Jt({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,f;if(e!==Pe.MUL)[h,f]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:x.dataId,dtype:x.dtype,shape:o.shape},k=new M2(e,i.shape,o.shape);return l.runWebGPUProgram(k,[A,b],fa(y.dtype,x.dtype))});else{let g=new jy(Pe.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new jy(Pe.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),x=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.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=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=wo({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=n||fa(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?T.fromUint8ToStringArray(c):c,f=i.dtype==="string"?T.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let p=new M2(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:Bse,castImpl:Wse,ceilImpl:Vse,concatImpl:Use,equalImpl:Gse,expImpl:Hse,expm1Impl:jse,floorImpl:qse,gatherNdImpl:Xse,gatherV2Impl:Kse,greaterEqualImpl:Zse,greaterImpl:Yse,lessEqualImpl:Jse,lessImpl:Qse,logImpl:eie,maxImpl:tie,maximumImpl:aie,minimumImpl:nie,multiplyImpl:rie,negImpl:sie,notEqualImpl:iie,prodImpl:oie,rangeImpl:lie,rsqrtImpl:uie,scatterImpl:die,simpleAbsImpl:pie,sliceImpl:cie,stridedSliceImpl:hie,stringNGramsImpl:fie,subImpl:mie,tileImpl:gie,topKImpl:yie,transposeImpl:xie,uniqueImpl:cfe}=Nh,Aie=et({opType:le.ABS,cpuKernelImpl:pie}),bie={kernelName:vl,backendName:"webgpu",kernelFunc:Aie},vie=et({opType:le.ACOS}),kie={kernelName:kl,backendName:"webgpu",kernelFunc:vie},wie=et({opType:le.ACOSH}),Iie={kernelName:wl,backendName:"webgpu",kernelFunc:wie},Sie=Jt({opType:Pe.ADD,cpuKernelImpl:Bse,supportsComplex:!0}),Tie={kernelName:as,backendName:"webgpu",kernelFunc:Sie},Cie=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return`
+      `}};function et({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new xu(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Jt({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,f;if(e!==Pe.MUL)[h,f]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:x.dataId,dtype:x.dtype,shape:o.shape},k=new M2(e,i.shape,o.shape);return l.runWebGPUProgram(k,[A,b],fa(y.dtype,x.dtype))});else{let g=new Hy(Pe.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new Hy(Pe.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),x=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.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=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=wo({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=n||fa(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?T.fromUint8ToStringArray(c):c,f=i.dtype==="string"?T.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let p=new M2(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:Bse,castImpl:Wse,ceilImpl:Vse,concatImpl:Use,equalImpl:Gse,expImpl:Hse,expm1Impl:jse,floorImpl:qse,gatherNdImpl:Xse,gatherV2Impl:Kse,greaterEqualImpl:Zse,greaterImpl:Yse,lessEqualImpl:Jse,lessImpl:Qse,logImpl:eie,maxImpl:tie,maximumImpl:aie,minimumImpl:nie,multiplyImpl:rie,negImpl:sie,notEqualImpl:iie,prodImpl:oie,rangeImpl:lie,rsqrtImpl:uie,scatterImpl:die,simpleAbsImpl:pie,sliceImpl:cie,stridedSliceImpl:hie,stringNGramsImpl:fie,subImpl:mie,tileImpl:gie,topKImpl:yie,transposeImpl:xie,uniqueImpl:cfe}=Ch,Aie=et({opType:le.ABS,cpuKernelImpl:pie}),bie={kernelName:vl,backendName:"webgpu",kernelFunc:Aie},vie=et({opType:le.ACOS}),kie={kernelName:kl,backendName:"webgpu",kernelFunc:vie},wie=et({opType:le.ACOSH}),Iie={kernelName:wl,backendName:"webgpu",kernelFunc:wie},Sie=Jt({opType:Pe.ADD,cpuKernelImpl:Bse,supportsComplex:!0}),Tie={kernelName:as,backendName:"webgpu",kernelFunc:Sie},Cie=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return`
       ${ke("index")} {
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5972,7 +5972,7 @@ return a / b;`,jK=`
           ${n}
         }
        }
-     `}};function Io(e,t,a,n,r){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=T.getAxesPermutation(l,s),p=e;u!=null&&(p=kr({inputs:{x:e},attrs:{perm:u},backend:r}),l=T.getInnerMostAxes(l.length,s),i.push(p)),T.assertAxesAreInnerMostDims(n,l,s);let[c,d]=T.computeOutAndReduceShapes(p.shape,l),h=c;a&&(h=T.expandShapeToKeepDim(c,o));let f;if((n==="max"||n==="prod")&&r.shouldExecuteOnCPU([p])){let m=r.tensorMap.get(p.dataId).values;switch(n){case"max":let g=tie(m,v.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=oie(p.shape,p.dtype,m,l);f=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(d),g=v.sizeFromShape(p.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},x=n==="mean"?"float32":Kd(e.dtype),A=[{type:"int32",data:[m]}],b=new Pie(y,n),k=r.runWebGPUProgram(b,[p],x,A);i.push(k),f=Ie({inputs:{x:k},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Fie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"all",a)}var Oie={kernelName:Zs,backendName:"webgpu",kernelFunc:Fie};function Die(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"any",a)}var zie={kernelName:Ys,backendName:"webgpu",kernelFunc:Die},$8=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=T.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=$e(this.outputShape),v.sizeFromShape(s)<32||v.sizeFromShape(r)>1e3?(this.type="plain",this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=we(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${br(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;r<this.outputShape.length;r++)n+=`outputCoords.${br(r)},`;return n};return this.type==="shared"?`
+     `}};function Io(e,t,a,n,r){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=T.getAxesPermutation(l,s),p=e;u!=null&&(p=kr({inputs:{x:e},attrs:{perm:u},backend:r}),l=T.getInnerMostAxes(l.length,s),i.push(p)),T.assertAxesAreInnerMostDims(n,l,s);let[c,d]=T.computeOutAndReduceShapes(p.shape,l),h=c;a&&(h=T.expandShapeToKeepDim(c,o));let f;if((n==="max"||n==="prod")&&r.shouldExecuteOnCPU([p])){let m=r.tensorMap.get(p.dataId).values;switch(n){case"max":let g=tie(m,v.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=oie(p.shape,p.dtype,m,l);f=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(d),g=v.sizeFromShape(p.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},x=n==="mean"?"float32":Xd(e.dtype),A=[{type:"int32",data:[m]}],b=new Pie(y,n),k=r.runWebGPUProgram(b,[p],x,A);i.push(k),f=Ie({inputs:{x:k},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Fie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"all",a)}var Oie={kernelName:Zs,backendName:"webgpu",kernelFunc:Fie};function Die(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"any",a)}var zie={kernelName:Ys,backendName:"webgpu",kernelFunc:Die},M8=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=T.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=$e(this.outputShape),v.sizeFromShape(s)<32||v.sizeFromShape(r)>1e3?(this.type="plain",this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=we(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${br(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;r<this.outputShape.length;r++)n+=`outputCoords.${br(r)},`;return n};return this.type==="shared"?`
       fn DIV_CEIL(a : u32, b : u32) -> u32 {
         return ((a - 1u) / b + 1u);
       }
@@ -6038,7 +6038,7 @@ return a / b;`,jK=`
           setOutputAtIndexI32(index, bestIndex);
         }
       }
-      `}};function Lie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=kr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new $8(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Bie={kernelName:Js,backendName:"webgpu",kernelFunc:Lie};function Wie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=kr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new $8(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Vie={kernelName:Sd,backendName:"webgpu",kernelFunc:Wie},Uie=et({opType:le.ASIN}),Gie={kernelName:Il,backendName:"webgpu",kernelFunc:Uie},Hie=et({opType:le.ASINH}),jie={kernelName:Sl,backendName:"webgpu",kernelFunc:Hie},qie=et({opType:le.ATAN}),Xie={kernelName:Tl,backendName:"webgpu",kernelFunc:qie},Kie=Jt({opType:Pe.ATAN2}),Zie={kernelName:Nl,backendName:"webgpu",kernelFunc:Kie},Yie=et({opType:le.ATANH}),Jie={kernelName:Cl,backendName:"webgpu",kernelFunc:Yie},qy=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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 / max(count, 1.0)"),`
+      `}};function Lie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=kr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new M8(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Bie={kernelName:Js,backendName:"webgpu",kernelFunc:Lie};function Wie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=kr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new M8(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Vie={kernelName:Id,backendName:"webgpu",kernelFunc:Wie},Uie=et({opType:le.ASIN}),Gie={kernelName:Il,backendName:"webgpu",kernelFunc:Uie},Hie=et({opType:le.ASINH}),jie={kernelName:Sl,backendName:"webgpu",kernelFunc:Hie},qie=et({opType:le.ATAN}),Xie={kernelName:Tl,backendName:"webgpu",kernelFunc:qie},Kie=Jt({opType:Pe.ATAN2}),Zie={kernelName:Nl,backendName:"webgpu",kernelFunc:Kie},Yie=et({opType:le.ATANH}),Jie={kernelName:Cl,backendName:"webgpu",kernelFunc:Yie},jy=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=$e(this.outputShape),this.dispatch=we(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 / max(count, 1.0)"),`
       ${ke("index")} {
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6086,7 +6086,7 @@ return a / b;`,jK=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function V3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return Io(r,s,i,"max",a)}var eoe={kernelName:$i,backendName:"webgpu",kernelFunc:V3};function _8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"mean",a)}var toe={kernelName:Fi,backendName:"webgpu",kernelFunc:_8};function P8(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return Ja({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=Ie({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=_8({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=V3({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=Ie({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Qie(t):(a==="avg"?r=new qy(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new qy(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function aoe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return P8(r,p,"avg",a)}var noe={kernelName:Qs,backendName:"webgpu",kernelFunc:aoe},roe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`stride : vec2<i32>, pads : vec2<i32>, dilation : vec2<i32>, filterDims : vec2<i32>,
+    `}};function V3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return Io(r,s,i,"max",a)}var eoe={kernelName:$i,backendName:"webgpu",kernelFunc:V3};function $8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"mean",a)}var toe={kernelName:Fi,backendName:"webgpu",kernelFunc:$8};function _8(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return Ja({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=Ie({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=$8({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=V3({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=Ie({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Qie(t):(a==="avg"?r=new jy(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new jy(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function aoe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return _8(r,p,"avg",a)}var noe={kernelName:Qs,backendName:"webgpu",kernelFunc:aoe},roe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`stride : vec2<i32>, pads : vec2<i32>, dilation : vec2<i32>, filterDims : vec2<i32>,
        outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avg_pool2d_backprop"}getUserCode(){return`
       ${ke("index")} {
       if (index < uniforms.size) {
@@ -6125,7 +6125,7 @@ return a / b;`,jK=`
         setOutputAtIndex(index, dotProd);
       }
     }
-    `}};function soe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;E8([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=new roe(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var ioe={kernelName:Kc,backendName:"webgpu",kernelFunc:soe};function ooe(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Wh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var loe={kernelName:ei,backendName:"webgpu",kernelFunc:ooe},uoe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${oa(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=oa(this.rank),t=doe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${$2[r]} = uniforms.start.${br(r)} + coords.${$2[r]};`),`
+    `}};function soe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;N8([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=new roe(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var ioe={kernelName:Xc,backendName:"webgpu",kernelFunc:soe};function ooe(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Bh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var loe={kernelName:ei,backendName:"webgpu",kernelFunc:ooe},uoe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${oa(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=oa(this.rank),t=doe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${$2[r]} = uniforms.start.${br(r)} + coords.${$2[r]};`),`
       ${ke("index")} {
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -6143,7 +6143,7 @@ return a / b;`,jK=`
   fn bincount_write(index: i32, value: f32) {
     atomicStore(&result[index], bitcast<i32>(value));
   }
-`,F8=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`
+`,P8=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`
     ${this.binaryOutput?moe:foe}
   ${ke("index")} {
     ${this.rank===1?`if (index < uniforms.xShape) {
@@ -6161,7 +6161,7 @@ return a / b;`,jK=`
       }
     }`}
   }
-  `}};function goe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Nr({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new F8([o],l),h=[{type:"int32",data:[i]}],f=l?[r,s]:[r];return a.runWebGPUProgram(d,f,p,h,c)}var yoe={kernelName:Td,backendName:"webgpu",kernelFunc:goe},O8=Jt({opType:Pe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:iie}),xoe={kernelName:Bi,backendName:"webgpu",kernelFunc:O8};function xp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return Ja({inputs:{x:r.complexTensorInfos.real},backend:a})}var Aoe={kernelName:zd,backendName:"webgpu",kernelFunc:xp};function boe(e,t){let a=new xu(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function _2(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Ja({inputs:{x:r},backend:a});let i=mn(r.shape),o=_2({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=wo({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=xp({inputs:{input:r},backend:a}),o=_2({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Ja({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=Wse(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return boe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=O8({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var voe={kernelName:ti,backendName:"webgpu",kernelFunc:_2},koe=et({opType:le.CEIL,cpuKernelImpl:Vse}),woe={kernelName:ai,backendName:"webgpu",kernelFunc:koe},Ioe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+  `}};function goe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Nr({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new P8([o],l),h=[{type:"int32",data:[i]}],f=l?[r,s]:[r];return a.runWebGPUProgram(d,f,p,h,c)}var yoe={kernelName:Sd,backendName:"webgpu",kernelFunc:goe},F8=Jt({opType:Pe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:iie}),xoe={kernelName:Bi,backendName:"webgpu",kernelFunc:F8};function yp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return Ja({inputs:{x:r.complexTensorInfos.real},backend:a})}var Aoe={kernelName:Dd,backendName:"webgpu",kernelFunc:yp};function boe(e,t){let a=new xu(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function _2(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Ja({inputs:{x:r},backend:a});let i=mn(r.shape),o=_2({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=wo({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=yp({inputs:{input:r},backend:a}),o=_2({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Ja({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=Wse(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return boe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=F8({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var voe={kernelName:ti,backendName:"webgpu",kernelFunc:_2},koe=et({opType:le.CEIL,cpuKernelImpl:Vse}),woe={kernelName:ai,backendName:"webgpu",kernelFunc:koe},Ioe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
       ${ke("index")} {
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -6196,7 +6196,7 @@ return a / b;`,jK=`
           }
         }
       }
-    `}};function Vh(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return Ja({inputs:{x:r.complexTensorInfos.imag},backend:a})}var Eoe={kernelName:Fd,backendName:"webgpu",kernelFunc:Vh};function Ju(e,t,a){let n=e[0].dtype;if(n==="complex64"){let f=e.map(A=>xp({inputs:{input:A},backend:a})),m=e.map(A=>Vh({inputs:{input:A},backend:a})),g=Ju(f,t,a),y=Ju(m,t,a),x=wo({inputs:{real:g,imag:y},backend:a});return f.forEach(A=>a.disposeData(A.dataId)),m.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),x}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let f=e.map(k=>{let S=[-1,v.sizeFromShape(k.shape.slice(t))];return Ie({inputs:{x:k},backend:a,attrs:{shape:S}})}),m=f.map(k=>({vals:a.readSync(k.dataId),shape:k.shape})),g=T.computeOutShape(f.map(k=>k.shape),1),y=f[0].shape[0]===1,x=Use(m,g,n,y),A=T.computeOutShape(e.map(k=>k.shape),t),b=a.makeTensorInfo(A,n,x);return f.forEach(k=>a.disposeData(k.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let f=[];for(let g=0;g<e.length;g+=s){let y=e.slice(g,g+s);f.push(Ju(y,t,a))}let m=Ju(f,t,a);for(let g of f)a.disposeData(g.dataId);return m}let{tensors2D:i,outShape:o}=Roe(e,t,a),l=i.map(f=>f.shape),u=new Noe(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let f=1;f<c.length;f++)c[f]=c[f-1]+l[f][1],p.push({type:"int32",data:[c[f]]})}let d=a.runWebGPUProgram(u,i,i[0].dtype,p);i.forEach(f=>a.disposeData(f.dataId));let h=Ie({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function Roe(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ie({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function D8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?Ja({inputs:{x:l[0]},backend:a}):Ju(l,s,a)}var Moe={kernelName:Rl,backendName:"webgpu",kernelFunc:D8};function $oe(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=N=>{switch(N){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},d=e?`
+    `}};function Wh(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return Ja({inputs:{x:r.complexTensorInfos.imag},backend:a})}var Eoe={kernelName:Pd,backendName:"webgpu",kernelFunc:Wh};function Yu(e,t,a){let n=e[0].dtype;if(n==="complex64"){let f=e.map(A=>yp({inputs:{input:A},backend:a})),m=e.map(A=>Wh({inputs:{input:A},backend:a})),g=Yu(f,t,a),y=Yu(m,t,a),x=wo({inputs:{real:g,imag:y},backend:a});return f.forEach(A=>a.disposeData(A.dataId)),m.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),x}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let f=e.map(k=>{let S=[-1,v.sizeFromShape(k.shape.slice(t))];return Ie({inputs:{x:k},backend:a,attrs:{shape:S}})}),m=f.map(k=>({vals:a.readSync(k.dataId),shape:k.shape})),g=T.computeOutShape(f.map(k=>k.shape),1),y=f[0].shape[0]===1,x=Use(m,g,n,y),A=T.computeOutShape(e.map(k=>k.shape),t),b=a.makeTensorInfo(A,n,x);return f.forEach(k=>a.disposeData(k.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let f=[];for(let g=0;g<e.length;g+=s){let y=e.slice(g,g+s);f.push(Yu(y,t,a))}let m=Yu(f,t,a);for(let g of f)a.disposeData(g.dataId);return m}let{tensors2D:i,outShape:o}=Roe(e,t,a),l=i.map(f=>f.shape),u=new Noe(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let f=1;f<c.length;f++)c[f]=c[f-1]+l[f][1],p.push({type:"int32",data:[c[f]]})}let d=a.runWebGPUProgram(u,i,i[0].dtype,p);i.forEach(f=>a.disposeData(f.dataId));let h=Ie({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function Roe(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ie({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function O8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?Ja({inputs:{x:l[0]},backend:a}):Yu(l,s,a)}var Moe={kernelName:Rl,backendName:"webgpu",kernelFunc:O8};function $oe(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=N=>{switch(N){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},d=e?`
       let coord = vec4<i32>(batch, xRow, xCol, xCh);
       `:`
       let coord = vec4<i32>(batch, xCh, xRow, xCol);
@@ -6265,7 +6265,7 @@ return a / b;`,jK=`
         ${ko(r,s)}
         setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
         }
-      }`}var _oe=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=D3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4<f32>"]):(this.innerElementSize=4,this.variableTypes=["vec4<f32>","vec4<f32>"]),r&&(this.variableNames.push("bias"),this.variableTypes.push("vec4<f32>")),i&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4<f32>"))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Lh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Bh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`
+      }`}var _oe=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!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,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=D3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4<f32>"]):(this.innerElementSize=4,this.variableTypes=["vec4<f32>","vec4<f32>"]),r&&(this.variableNames.push("bias"),this.variableTypes.push("vec4<f32>")),i&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4<f32>"))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?zh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Lh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`
     ${$oe(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}
     ${e}
   `}},Poe=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`
@@ -6338,7 +6338,7 @@ return a / b;`,jK=`
         setOutputAtIndex(index, value);
       }
     }
-   `}};function Gc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function Ooe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,f;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=Ie({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=Ie({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(f),s!=null){let y=Gc(s.shape,l);y!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:y}}),d.push(s))}if(r!=null){let y=Gc(r.shape,l);y!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:y}}),d.push(r))}let m=Wh({a:l?h:f,b:l?f:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=Ie({inputs:{x:m},backend:n,attrs:{shape:a.outShape}});d.push(m);for(let y of d)n.disposeData(y.dataId);return g}function Doe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:x}=a,A=x==="channelsLast",b=l*u*p,k=m*f,S=A?[a.batchSize,k,b]:[a.batchSize,b,k],C=new Foe(S,A),N=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[y,g]},{type:"int32",data:[f]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],$=n.runWebGPUProgram(C,[e],e.dtype,N),M=[];M.push($);let R=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if(M.push(R),s!=null){let D=Gc(s.shape,A);D!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:D}}),M.push(s))}if(r!=null){let D=Gc(r.shape,A);D!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:D}}),M.push(r))}let I=Wh({a:A?$:R,b:A?R:$,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=Ie({inputs:{x:I},backend:n,attrs:{shape:a.outShape}});M.push(I);for(let D of M)n.disposeData(D.dataId);return _}function z8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=V().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return Ooe({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=V().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=h>0?h:n.thresholdToIncreaseWorkgroups,m=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(V().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||m<=f)return Doe({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,y=[a.padInfo.top,a.padInfo.left],x=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...y]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new Poe(a,l,o,u);else{let S=p?a.outHeight*a.outWidth:a.outChannels,C=p?a.outChannels:a.outHeight*a.outWidth,N=a.filterHeight*a.filterWidth*a.inChannels;x.push({type:"int32",data:[S]},{type:"int32",data:[C]},{type:"int32",data:[N]});let $=n.adapterInfo.isIntel();g=new _oe(a,S,C,N,l,o,u,$)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(x.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let k=n.runWebGPUProgram(g,b,e.dtype,x);for(let S of A)n.disposeData(S.dataId);return k}function zoe(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return z8({x:r,filter:s,convInfo:d,backend:n})}var Loe={kernelName:ni,backendName:"webgpu",kernelFunc:zoe},Boe=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=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1;return`
+   `}};function Uc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function Ooe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,f;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=Ie({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=Ie({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(f),s!=null){let y=Uc(s.shape,l);y!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:y}}),d.push(s))}if(r!=null){let y=Uc(r.shape,l);y!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:y}}),d.push(r))}let m=Bh({a:l?h:f,b:l?f:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=Ie({inputs:{x:m},backend:n,attrs:{shape:a.outShape}});d.push(m);for(let y of d)n.disposeData(y.dataId);return g}function Doe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:x}=a,A=x==="channelsLast",b=l*u*p,k=m*f,S=A?[a.batchSize,k,b]:[a.batchSize,b,k],C=new Foe(S,A),N=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[y,g]},{type:"int32",data:[f]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],$=n.runWebGPUProgram(C,[e],e.dtype,N),M=[];M.push($);let R=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if(M.push(R),s!=null){let D=Uc(s.shape,A);D!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:D}}),M.push(s))}if(r!=null){let D=Uc(r.shape,A);D!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:D}}),M.push(r))}let I=Bh({a:A?$:R,b:A?R:$,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=Ie({inputs:{x:I},backend:n,attrs:{shape:a.outShape}});M.push(I);for(let D of M)n.disposeData(D.dataId);return _}function D8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=V().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return Ooe({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=V().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=h>0?h:n.thresholdToIncreaseWorkgroups,m=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(V().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||m<=f)return Doe({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,y=[a.padInfo.top,a.padInfo.left],x=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...y]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new Poe(a,l,o,u);else{let S=p?a.outHeight*a.outWidth:a.outChannels,C=p?a.outChannels:a.outHeight*a.outWidth,N=a.filterHeight*a.filterWidth*a.inChannels;x.push({type:"int32",data:[S]},{type:"int32",data:[C]},{type:"int32",data:[N]});let $=n.adapterInfo.isIntel();g=new _oe(a,S,C,N,l,o,u,$)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(x.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let k=n.runWebGPUProgram(g,b,e.dtype,x);for(let S of A)n.disposeData(S.dataId);return k}function zoe(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return D8({x:r,filter:s,convInfo:d,backend:n})}var Loe={kernelName:ni,backendName:"webgpu",kernelFunc:zoe},Boe=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=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1;return`
     ${ke("index")} {
       if(index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6428,7 +6428,7 @@ return a / b;`,jK=`
         setOutputAtIndex(index, dotProd);
       }
     }
-  `}};function Voe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new Woe(d),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,f)}var Uoe={kernelName:Nd,backendName:"webgpu",kernelFunc:Voe};function Goe(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return`
+  `}};function Voe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new Woe(d),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,f)}var Uoe={kernelName:Cd,backendName:"webgpu",kernelFunc:Voe};function Goe(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return`
             let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
             let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
             let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
@@ -6491,7 +6491,7 @@ return a / b;`,jK=`
           col);
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${e}] = value;
     }
-  }`}var Hoe=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,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=D3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4<f32>","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Lh(this.elementsPerThread,this.workgroupSize):Bh(this.elementsPerThread,this.workgroupSize);return`
+  }`}var Hoe=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,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=D3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4<f32>","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?zh(this.elementsPerThread,this.workgroupSize):Lh(this.elementsPerThread,this.workgroupSize);return`
     ${Goe(this.isVec4?4:1)}
     ${e}
     `}};function joe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(V().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.filterHeight<=2&&d.filterWidth<=2&&d.outChannels<=16&&d.inChannels===1)f=new Boe(d);else{f=new Hoe(d);let m=d.inHeight*d.inWidth,g=d.inChannels,y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return a.runWebGPUProgram(f,[r,s],"float32",h)}var qoe={kernelName:ri,backendName:"webgpu",kernelFunc:joe},Xoe=et({opType:le.COS}),Koe={kernelName:si,backendName:"webgpu",kernelFunc:Xoe},Zoe=et({opType:le.COSH}),Yoe={kernelName:ii,backendName:"webgpu",kernelFunc:Zoe},Joe=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
@@ -6550,23 +6550,23 @@ return a / b;`,jK=`
         }
       }
     }
-    `}},Qoe=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Joe(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},ele={kernelName:ui,backendName:"webgpu",kernelFunc:Qoe},kd;(function(e){e.Prod="*",e.Sum="+"})(kd||(kd={}));var Xy=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===kd.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${Ky(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),`
+    `}},Qoe=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Joe(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},ele={kernelName:ui,backendName:"webgpu",kernelFunc:Qoe},vd;(function(e){e.Prod="*",e.Sum="+"})(vd||(vd={}));var qy=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===vd.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${Xy(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),`
       ${ke("index")} {
        if (index < uniforms.size) {
          var coords = getCoordsFromIndex(index);
 
-         let end = ${Zy(e,"coords",this.op)};
+         let end = ${Ky(e,"coords",this.op)};
          var val = ${a};
          let pow2 = i32(pow(2.0, uniforms.index));
          if (${r}) {
            let idx = ${s};
-           ${Zy(e,"coords",this.op)} = idx;
-           val ${this.op}= getX(${Ky(e,"coords",this.op)});
+           ${Ky(e,"coords",this.op)} = idx;
+           val ${this.op}= getX(${Xy(e,"coords",this.op)});
          }
          setOutputAtIndex(index, val);
        }
       }
-    `}};function Ky(e,t,a){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 ${a} for rank ${e} is not yet supported`)}function Zy(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function L8(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=kr({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Ja({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new Xy(e,l.shape,!1,s),f=c,m=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,m),a.disposeData(f.dataId)}if(r){let d=new Xy(e,l.shape,r,s),h=c,f=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,f),a.disposeData(h.dataId)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=kr({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function tle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return L8(kd.Prod,r,a,s,i,o)}var ale={kernelName:oi,backendName:"webgpu",kernelFunc:tle};function nle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return L8(kd.Sum,r,a,s,i,o)}var rle={kernelName:li,backendName:"webgpu",kernelFunc:nle};function sle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Nr({backend:a,attrs:{shape:d,value:0,dtype:p}}),f=new F8(c,u,o),m=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(f,g,p,m,h)}var ile={kernelName:Ed,backendName:"webgpu",kernelFunc:sle},ole=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+    `}};function Xy(e,t,a){if(e===1)return`${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 ${a} for rank ${e} is not yet supported`)}function Ky(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function z8(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=kr({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Ja({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new qy(e,l.shape,!1,s),f=c,m=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,m),a.disposeData(f.dataId)}if(r){let d=new qy(e,l.shape,r,s),h=c,f=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,f),a.disposeData(h.dataId)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=kr({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function tle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return z8(vd.Prod,r,a,s,i,o)}var ale={kernelName:oi,backendName:"webgpu",kernelFunc:tle};function nle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return z8(vd.Sum,r,a,s,i,o)}var rle={kernelName:li,backendName:"webgpu",kernelFunc:nle};function sle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Nr({backend:a,attrs:{shape:d,value:0,dtype:p}}),f=new P8(c,u,o),m=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(f,g,p,m,h)}var ile={kernelName:Nd,backendName:"webgpu",kernelFunc:sle},ole=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
       ${ke("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6648,7 +6648,7 @@ return a / b;`,jK=`
           setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
         }
       }
-    `}},B8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, inDims : vec2<i32>,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,a=this.convInfo.strideWidth;return`
+    `}},L8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, inDims : vec2<i32>,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,a=this.convInfo.strideWidth;return`
       ${Cr(this.activation,this.hasPreluActivation,!0,4)}
       fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {
         var value = vec4<f32>(0.0);
@@ -6698,7 +6698,7 @@ return a / b;`,jK=`
           }
         }
       }
-    `}},W8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
+    `}},B8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
       filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return`
       ${Cr(this.activation,this.hasPreluActivation,!1,4)}
 
@@ -6765,7 +6765,7 @@ return a / b;`,jK=`
           setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
         }
       }
-    `}};function ple(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=T.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),f=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],m=h.dataFormat==="channelsLast",g;return!m&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new dle(h.outShape,h.filterHeight,h.filterWidth):m&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?g=new B8(h):(g=new W8(h),f.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,f)}var cle={kernelName:pi,backendName:"webgpu",kernelFunc:ple},hle=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return`
+    `}};function ple(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=T.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),f=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],m=h.dataFormat==="channelsLast",g;return!m&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new dle(h.outShape,h.filterHeight,h.filterWidth):m&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?g=new L8(h):(g=new B8(h),f.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,f)}var cle={kernelName:pi,backendName:"webgpu",kernelFunc:ple},hle=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return`
       ${ke("index")} {
         if (index < uniforms.size) {
           let coords = getOutputCoords();
@@ -6773,7 +6773,7 @@ return a / b;`,jK=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function fle(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=Ie({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new hle(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=Ie({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var mle={kernelName:Rd,backendName:"webgpu",kernelFunc:fle},gle=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return`
+    `}};function fle(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=Ie({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new hle(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=Ie({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var mle={kernelName:Ed,backendName:"webgpu",kernelFunc:fle},gle=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return`
        ${ke("index")} {
          if (index < uniforms.size) {
            let neg_infinity = -3.4e38;
@@ -6805,7 +6805,7 @@ return a / b;`,jK=`
            setOutputAtIndex(index, curVal);
          }
        }
-     `}};function yle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new gle(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var xle={kernelName:Md,backendName:"webgpu",kernelFunc:yle},V8=Jt({opType:Pe.MUL,cpuKernelImpl:rie,supportsComplex:!0}),Ale={kernelName:Li,backendName:"webgpu",kernelFunc:V8};function U3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"sum",a)}var ble={kernelName:io,backendName:"webgpu",kernelFunc:U3};function vle(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m<c;++m){for(let g of p[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=s[g]:(A=kr({inputs:{x:s[g]},backend:a,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let k=0;k<x.length;++k)b.splice(x[k],0,1);v.arraysEqual(A.shape,b)||(A=Ie({inputs:{x:A},backend:a,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=V8({inputs:{a:A,b:d},backend:a}),f.push(d))}m<c-1&&(u[m]>=0&&(d=U3({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeData(m.dataId);return d}var kle={kernelName:$d,backendName:"webgpu",kernelFunc:vle},wle=et({opType:le.ELU}),Ile={kernelName:hi,backendName:"webgpu",kernelFunc:wle},Sle=Jt({opType:Pe.EQUAL,dtype:"bool",cpuKernelImpl:Gse}),Tle={kernelName:fi,backendName:"webgpu",kernelFunc:Sle},Cle=et({opType:le.ERF}),Nle={kernelName:Ml,backendName:"webgpu",kernelFunc:Cle},U8=et({opType:le.EXP,cpuKernelImpl:Hse,dtype:"float32"}),Ele={kernelName:mi,backendName:"webgpu",kernelFunc:U8};function P2(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ie({inputs:{x:s},backend:n,attrs:{shape:o}})}var Rle={kernelName:$l,backendName:"webgpu",kernelFunc:P2},Mle=et({opType:le.EXPM1,cpuKernelImpl:jse}),$le={kernelName:_l,backendName:"webgpu",kernelFunc:Mle},Yy=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`
+     `}};function yle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new gle(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var xle={kernelName:Rd,backendName:"webgpu",kernelFunc:yle},W8=Jt({opType:Pe.MUL,cpuKernelImpl:rie,supportsComplex:!0}),Ale={kernelName:Li,backendName:"webgpu",kernelFunc:W8};function U3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"sum",a)}var ble={kernelName:io,backendName:"webgpu",kernelFunc:U3};function vle(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m<c;++m){for(let g of p[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=s[g]:(A=kr({inputs:{x:s[g]},backend:a,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let k=0;k<x.length;++k)b.splice(x[k],0,1);v.arraysEqual(A.shape,b)||(A=Ie({inputs:{x:A},backend:a,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=W8({inputs:{a:A,b:d},backend:a}),f.push(d))}m<c-1&&(u[m]>=0&&(d=U3({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeData(m.dataId);return d}var kle={kernelName:Md,backendName:"webgpu",kernelFunc:vle},wle=et({opType:le.ELU}),Ile={kernelName:hi,backendName:"webgpu",kernelFunc:wle},Sle=Jt({opType:Pe.EQUAL,dtype:"bool",cpuKernelImpl:Gse}),Tle={kernelName:fi,backendName:"webgpu",kernelFunc:Sle},Cle=et({opType:le.ERF}),Nle={kernelName:Ml,backendName:"webgpu",kernelFunc:Cle},V8=et({opType:le.EXP,cpuKernelImpl:Hse,dtype:"float32"}),Ele={kernelName:mi,backendName:"webgpu",kernelFunc:V8};function P2(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ie({inputs:{x:s},backend:n,attrs:{shape:o}})}var Rle={kernelName:$l,backendName:"webgpu",kernelFunc:P2},Mle=et({opType:le.EXPM1,cpuKernelImpl:jse}),$le={kernelName:_l,backendName:"webgpu",kernelFunc:Mle},Zy=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`
     fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {
       ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"}
     }
@@ -6838,7 +6838,7 @@ return a / b;`,jK=`
         setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));
       }
     }
-  `}};function G8(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=Ie({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new Yy("real",u),c=new Yy("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,m=[{type:"float32",data:[h]},{type:"float32",data:[f]}],g=a.runWebGPUProgram(p,d,"float32",m);o.push(g);let y=a.runWebGPUProgram(c,d,"float32",m);o.push(y);let x=wo({inputs:{real:g,imag:y},backend:a});o.push(x);let A=Ie({inputs:{x},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function _le(e){let{inputs:t,backend:a}=e,{input:n}=t;return G8(n,!1,a)}var Ple={kernelName:_d,backendName:"webgpu",kernelFunc:_le},Fle=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+  `}};function U8(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=Ie({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new Zy("real",u),c=new Zy("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,m=[{type:"float32",data:[h]},{type:"float32",data:[f]}],g=a.runWebGPUProgram(p,d,"float32",m);o.push(g);let y=a.runWebGPUProgram(c,d,"float32",m);o.push(y);let x=wo({inputs:{real:g,imag:y},backend:a});o.push(x);let A=Ie({inputs:{x},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function _le(e){let{inputs:t,backend:a}=e,{input:n}=t;return U8(n,!1,a)}var Ple={kernelName:$d,backendName:"webgpu",kernelFunc:_le},Fle=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
       ${ke("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6859,7 +6859,7 @@ return a / b;`,jK=`
           }
         }
       }
-  `}},Vle={kernelName:rd,backendName:"webgpu",kernelFunc:Ule},Zo,zm=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"),gc=new Map;function Ule(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n;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,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[p,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[c,p,s],h=!1,f=i||o;if(u||l||f){let x;if(h){let M=r;if(!gc.has(M)||gc.get(M).expired){let R={source:M};gc.set(M,a.device.importExternalTexture(R))}x={width:p,height:c,format:null,usage:null,texture:gc.get(M)}}else{if(f){let _=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Zo==null||_!==zm)&&(zm=_,Zo=document.createElement("canvas").getContext("2d",{willReadFrequently:zm})),Zo.canvas.width=p,Zo.canvas.height=c,Zo.drawImage(r,0,0,p,c),r=Zo.canvas}let M=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,R="rgba8unorm",I=a.textureManager.acquireTexture(d[1],d[0],R,M);a.queue.copyExternalImageToTexture({source:r},{texture:I},[d[1],d[0]]),x={width:p,height:c,format:R,usage:M,texture:I}}let A=v.sizeFromShape(d),b=v.computeStrides(d),k=new Wle(d,s,h),S=[{type:"uint32",data:[A]},{type:"uint32",data:[s]},{type:"uint32",data:[...b]}],C=a.makeTensorInfo([c,p],"int32"),N=a.tensorMap.get(C.dataId);N.resourceInfo=x;let $=a.runWebGPUProgram(k,[C],"int32",S);return a.disposeData(C.dataId),$}let m=r.data,g=m;if(s!=null&&s!==4){g=new Uint8Array(r.width*r.height*s);let x=m.length,A=0;for(let b=0;b<x;b++)b%4<s&&(g[A++]=m[b])}let y=a.makeTensorInfo(d,"int32",new Int32Array(g));return a.uploadToGPU(y.dataId),y}var Gle=class{constructor(e,t,a,n,r){this.uniforms="varianceEpsilon : f32,",this.workgroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a),this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=n,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)"),`
+  `}},Vle={kernelName:nd,backendName:"webgpu",kernelFunc:Ule},Zo,zm=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"),mc=new Map;function Ule(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n;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,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[p,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[c,p,s],h=!1,f=i||o;if(u||l||f){let x;if(h){let M=r;if(!mc.has(M)||mc.get(M).expired){let R={source:M};mc.set(M,a.device.importExternalTexture(R))}x={width:p,height:c,format:null,usage:null,texture:mc.get(M)}}else{if(f){let _=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Zo==null||_!==zm)&&(zm=_,Zo=document.createElement("canvas").getContext("2d",{willReadFrequently:zm})),Zo.canvas.width=p,Zo.canvas.height=c,Zo.drawImage(r,0,0,p,c),r=Zo.canvas}let M=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,R="rgba8unorm",I=a.textureManager.acquireTexture(d[1],d[0],R,M);a.queue.copyExternalImageToTexture({source:r},{texture:I},[d[1],d[0]]),x={width:p,height:c,format:R,usage:M,texture:I}}let A=v.sizeFromShape(d),b=v.computeStrides(d),k=new Wle(d,s,h),S=[{type:"uint32",data:[A]},{type:"uint32",data:[s]},{type:"uint32",data:[...b]}],C=a.makeTensorInfo([c,p],"int32"),N=a.tensorMap.get(C.dataId);N.resourceInfo=x;let $=a.runWebGPUProgram(k,[C],"int32",S);return a.disposeData(C.dataId),$}let m=r.data,g=m;if(s!=null&&s!==4){g=new Uint8Array(r.width*r.height*s);let x=m.length,A=0;for(let b=0;b<x;b++)b%4<s&&(g[A++]=m[b])}let y=a.makeTensorInfo(d,"int32",new Int32Array(g));return a.uploadToGPU(y.dataId),y}var Gle=class{constructor(e,t,a,n,r){this.uniforms="varianceEpsilon : f32,",this.workgroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a),this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=n,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)"),`
       ${ke("index")} {
         if (index < uniforms.size)
         {
@@ -6872,7 +6872,7 @@ return a / b;`,jK=`
           setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
         }
       }
-  `}},Hle={kernelName:Ai,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new Gle(n.shape,i.shape,o.shape,c,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,f)}};function jle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(p),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,m);return z8({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var qle={kernelName:qr,backendName:"webgpu",kernelFunc:jle};function Xle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,f=p;f==null&&(f=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=T.computeConv2DInfo(r.shape,s.shape,l,f,u,c,!0),g=[r,s],y=i!=null,x=o!=null;y&&g.push(i),x&&g.push(o);let A=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.outHeight>4&&m.outWidth>4&&m.strideWidth<=2&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.inChannels%4===0?b=new B8(m,y,d,x):(b=new W8(m,y,d,x),A.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var Kle={kernelName:Xr,backendName:"webgpu",kernelFunc:Xle},Zle=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${oa(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+  `}},Hle={kernelName:Ai,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new Gle(n.shape,i.shape,o.shape,c,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,f)}};function jle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(p),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,m);return D8({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var qle={kernelName:qr,backendName:"webgpu",kernelFunc:jle};function Xle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,f=p;f==null&&(f=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=T.computeConv2DInfo(r.shape,s.shape,l,f,u,c,!0),g=[r,s],y=i!=null,x=o!=null;y&&g.push(i),x&&g.push(o);let A=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.outHeight>4&&m.outWidth>4&&m.strideWidth<=2&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.inChannels%4===0?b=new L8(m,y,d,x):(b=new B8(m,y,d,x),A.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var Kle={kernelName:Xr,backendName:"webgpu",kernelFunc:Xle},Zle=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${oa(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
       ${ke("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6895,13 +6895,13 @@ return a / b;`,jK=`
           setOutputAtIndex(index, inBounds * getA(${e}));
         }
       }
-    `}};function eue(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;n<e.length;n++)n===2?a.push("indexZ"):a.push(`${t[n]}`);return a.join()}function H8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0],u=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=v.sizeFromShape(s.shape),c=[],d=Ie({inputs:{x:r},backend:a,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=Ie({inputs:{x:s},backend:a,attrs:{shape:[u.batchSize,p/u.batchSize]}});c.push(d),c.push(h);let f=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(a.shouldExecuteOnCPU([r,s])){let x=a.tensorMap.get(h.dataId).values,A=_e(h.shape,h.dtype,x),b=a.tensorMap.get(d.dataId).values,k=_e(d.shape,d.dtype,b),S=Kse(k,A,f);return c.forEach(C=>a.disposeData(C.dataId)),a.makeTensorInfo(u.outputShape,S.dtype,S.values)}let m=new Qle(d.shape,f),g=a.runWebGPUProgram(m,[d,h],d.dtype);c.push(g);let y=Ie({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeData(x.dataId)),y}var tue={kernelName:Fl,backendName:"webgpu",kernelFunc:H8},aue=Jt({opType:Pe.GREATER,cpuKernelImpl:Yse,dtype:"bool"}),nue={kernelName:vi,backendName:"webgpu",kernelFunc:aue},rue=Jt({opType:Pe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Zse}),sue={kernelName:ki,backendName:"webgpu",kernelFunc:rue};function iue(e){let{inputs:t,backend:a}=e,{input:n}=t;return G8(n,!0,a)}var oue={kernelName:Pd,backendName:"webgpu",kernelFunc:iue},lue=et({opType:le.IS_FINITE,dtype:"bool"}),uue={kernelName:Ol,backendName:"webgpu",kernelFunc:lue},due=et({opType:le.IS_INF,dtype:"bool"}),pue={kernelName:Dl,backendName:"webgpu",kernelFunc:due},cue=et({opType:le.IS_NAN,dtype:"bool"}),hue={kernelName:Ii,backendName:"webgpu",kernelFunc:cue};function fue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new xu(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var mue={kernelName:Si,backendName:"webgpu",kernelFunc:fue},gue=Jt({opType:Pe.LESS,dtype:"bool",cpuKernelImpl:Qse}),yue={kernelName:Ti,backendName:"webgpu",kernelFunc:gue},xue=Jt({opType:Pe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Jse}),Aue={kernelName:Ci,backendName:"webgpu",kernelFunc:xue},bue=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return`
+    `}};function eue(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;n<e.length;n++)n===2?a.push("indexZ"):a.push(`${t[n]}`);return a.join()}function G8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0],u=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=v.sizeFromShape(s.shape),c=[],d=Ie({inputs:{x:r},backend:a,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=Ie({inputs:{x:s},backend:a,attrs:{shape:[u.batchSize,p/u.batchSize]}});c.push(d),c.push(h);let f=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(a.shouldExecuteOnCPU([r,s])){let x=a.tensorMap.get(h.dataId).values,A=_e(h.shape,h.dtype,x),b=a.tensorMap.get(d.dataId).values,k=_e(d.shape,d.dtype,b),S=Kse(k,A,f);return c.forEach(C=>a.disposeData(C.dataId)),a.makeTensorInfo(u.outputShape,S.dtype,S.values)}let m=new Qle(d.shape,f),g=a.runWebGPUProgram(m,[d,h],d.dtype);c.push(g);let y=Ie({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeData(x.dataId)),y}var tue={kernelName:Fl,backendName:"webgpu",kernelFunc:G8},aue=Jt({opType:Pe.GREATER,cpuKernelImpl:Yse,dtype:"bool"}),nue={kernelName:vi,backendName:"webgpu",kernelFunc:aue},rue=Jt({opType:Pe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Zse}),sue={kernelName:ki,backendName:"webgpu",kernelFunc:rue};function iue(e){let{inputs:t,backend:a}=e,{input:n}=t;return U8(n,!0,a)}var oue={kernelName:_d,backendName:"webgpu",kernelFunc:iue},lue=et({opType:le.IS_FINITE,dtype:"bool"}),uue={kernelName:Ol,backendName:"webgpu",kernelFunc:lue},due=et({opType:le.IS_INF,dtype:"bool"}),pue={kernelName:Dl,backendName:"webgpu",kernelFunc:due},cue=et({opType:le.IS_NAN,dtype:"bool"}),hue={kernelName:Ii,backendName:"webgpu",kernelFunc:cue};function fue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new xu(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var mue={kernelName:Si,backendName:"webgpu",kernelFunc:fue},gue=Jt({opType:Pe.LESS,dtype:"bool",cpuKernelImpl:Qse}),yue={kernelName:Ti,backendName:"webgpu",kernelFunc:gue},xue=Jt({opType:Pe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Jse}),Aue={kernelName:Ci,backendName:"webgpu",kernelFunc:xue},bue=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return`
       ${ke("index")} {
         if (index < uniforms.size) {
           setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);
         }
       }
-    `}};function vue(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new bue(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var kue={kernelName:Od,backendName:"webgpu",kernelFunc:vue},wue=et({opType:le.LOG,cpuKernelImpl:eie}),Iue={kernelName:Ni,backendName:"webgpu",kernelFunc:wue},Sue=et({opType:le.LOG1P}),Tue={kernelName:zl,backendName:"webgpu",kernelFunc:Sue},Cue=Jt({opType:Pe.LOGICAL_AND,dtype:"bool"}),Nue={kernelName:Ei,backendName:"webgpu",kernelFunc:Cue},Eue=et({opType:le.LOGICAL_NOT}),Rue={kernelName:Ri,backendName:"webgpu",kernelFunc:Eue},Mue=Jt({opType:Pe.LOGICAL_OR}),$ue={kernelName:Mi,backendName:"webgpu",kernelFunc:Mue},j8=`
+    `}};function vue(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new bue(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var kue={kernelName:Fd,backendName:"webgpu",kernelFunc:vue},wue=et({opType:le.LOG,cpuKernelImpl:eie}),Iue={kernelName:Ni,backendName:"webgpu",kernelFunc:wue},Sue=et({opType:le.LOG1P}),Tue={kernelName:zl,backendName:"webgpu",kernelFunc:Sue},Cue=Jt({opType:Pe.LOGICAL_AND,dtype:"bool"}),Nue={kernelName:Ei,backendName:"webgpu",kernelFunc:Cue},Eue=et({opType:le.LOGICAL_NOT}),Rue={kernelName:Ri,backendName:"webgpu",kernelFunc:Eue},Mue=Jt({opType:Pe.LOGICAL_OR}),$ue={kernelName:Mi,backendName:"webgpu",kernelFunc:Mue},H8=`
   var powValue = 0.0;
   let basis = uniforms.bias + uniforms.alpha * sum;
   if (uniforms.beta == 0.5) {
@@ -6929,7 +6929,7 @@ return a / b;`,jK=`
             sum = sum + z * z;
           }
         }
-        ${j8}
+        ${H8}
 
         setOutputAtIndex(index, x * powValue);
       }
@@ -6962,11 +6962,11 @@ return a / b;`,jK=`
           let z = lrnSub[index + i];
           sum = sum + z * z;
         }
-        ${j8}
+        ${H8}
 
         setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);
       }
-    } `}};function Fue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new _ue(r.shape):u=new Pue(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Oue={kernelName:Dd,backendName:"webgpu",kernelFunc:Fue},Due=Jt({opType:Pe.MAX,cpuKernelImpl:aie}),zue={kernelName:_i,backendName:"webgpu",kernelFunc:Due};function Lue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return P8(r,p,"max",a)}var Bue={kernelName:Pi,backendName:"webgpu",kernelFunc:Lue};function Wue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"min",a)}var Vue={kernelName:Oi,backendName:"webgpu",kernelFunc:Wue},Uue=Jt({opType:Pe.MIN,cpuKernelImpl:nie}),Gue={kernelName:Di,backendName:"webgpu",kernelFunc:Uue},Hue=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=oa(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    } `}};function Fue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new _ue(r.shape):u=new Pue(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Oue={kernelName:Od,backendName:"webgpu",kernelFunc:Fue},Due=Jt({opType:Pe.MAX,cpuKernelImpl:aie}),zue={kernelName:_i,backendName:"webgpu",kernelFunc:Due};function Lue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return _8(r,p,"max",a)}var Bue={kernelName:Pi,backendName:"webgpu",kernelFunc:Lue};function Wue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"min",a)}var Vue={kernelName:Oi,backendName:"webgpu",kernelFunc:Wue},Uue=Jt({opType:Pe.MIN,cpuKernelImpl:nie}),Gue={kernelName:Di,backendName:"webgpu",kernelFunc:Uue},Hue=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=oa(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${ke("index")} {
         if (index < uniforms.size) {
           let start = ${i}(${t});
@@ -6991,7 +6991,7 @@ return a / b;`,jK=`
                                       f32(i32(round(getX(coords.x))) == coords.y)));
         }
       }
-    `}};function ade(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new tde(u,i),c=Ie({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let f=[...r.shape,i],m=Ie({inputs:{x:h},backend:a,attrs:{shape:f}});return a.disposeData(h.dataId),m}var nde={kernelName:Ui,backendName:"webgpu",kernelFunc:ade};function Hc(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=xp({inputs:{input:n},backend:a}),s=Hc({inputs:{x:r},backend:a}),i=Vh({inputs:{input:n},backend:a}),o=Hc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Nr({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var rde={kernelName:nu,backendName:"webgpu",kernelFunc:Hc};function q8(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=xp({inputs:{input:n},backend:a}),s=q8({inputs:{x:r},backend:a}),i=Vh({inputs:{input:n},backend:a}),o=Hc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Nr({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var sde={kernelName:Vl,backendName:"webgpu",kernelFunc:q8};function ide(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return P2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=P2({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=D8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var ode={kernelName:Ul,backendName:"webgpu",kernelFunc:ide},lde=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=oa(e),a=this.xShape.map((u,p)=>`uniforms.pad${p}[0]`).join(","),n=this.xShape.map((u,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${a})`:`${a}`,s=e>1?`${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`
+    `}};function ade(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new tde(u,i),c=Ie({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let f=[...r.shape,i],m=Ie({inputs:{x:h},backend:a,attrs:{shape:f}});return a.disposeData(h.dataId),m}var nde={kernelName:Ui,backendName:"webgpu",kernelFunc:ade};function Gc(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=yp({inputs:{input:n},backend:a}),s=Gc({inputs:{x:r},backend:a}),i=Wh({inputs:{input:n},backend:a}),o=Gc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Nr({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var rde={kernelName:nu,backendName:"webgpu",kernelFunc:Gc};function j8(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=yp({inputs:{input:n},backend:a}),s=j8({inputs:{x:r},backend:a}),i=Wh({inputs:{input:n},backend:a}),o=Gc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Nr({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var sde={kernelName:Vl,backendName:"webgpu",kernelFunc:j8};function ide(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return P2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=P2({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=O8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var ode={kernelName:Ul,backendName:"webgpu",kernelFunc:ide},lde=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=oa(e),a=this.xShape.map((u,p)=>`uniforms.pad${p}[0]`).join(","),n=this.xShape.map((u,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${a})`:`${a}`,s=e>1?`${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`
       ${ke("index")} {
         if (index < uniforms.size) {
           let start = ${r};
@@ -7006,7 +7006,7 @@ return a / b;`,jK=`
           }
         }
       }
-    `}},X8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return Ja({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Nr({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new lde(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},ude={kernelName:Gi,backendName:"webgpu",kernelFunc:X8},dde=Jt({opType:Pe.POW}),pde={kernelName:Hi,backendName:"webgpu",kernelFunc:dde};function cde(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new M2(Pe.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var hde={kernelName:ji,backendName:"webgpu",kernelFunc:cde};function fde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"prod",a)}var mde={kernelName:qi,backendName:"webgpu",kernelFunc:fde},gde=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=lie(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},yde={kernelName:Gl,backendName:"webgpu",kernelFunc:gde},K8=Jt({opType:Pe.DIV}),xde={kernelName:ci,backendName:"webgpu",kernelFunc:K8},Ade=et({opType:le.RECIPROCAL}),bde={kernelName:Xi,backendName:"webgpu",kernelFunc:Ade},vde=et({opType:le.RELU}),kde={kernelName:Ki,backendName:"webgpu",kernelFunc:vde},wde=et({opType:le.RELU6}),Ide={kernelName:Ji,backendName:"webgpu",kernelFunc:wde},Sde=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+    `}},q8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return Ja({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Nr({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new lde(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},ude={kernelName:Gi,backendName:"webgpu",kernelFunc:q8},dde=Jt({opType:Pe.POW}),pde={kernelName:Hi,backendName:"webgpu",kernelFunc:dde};function cde(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new M2(Pe.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var hde={kernelName:ji,backendName:"webgpu",kernelFunc:cde};function fde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"prod",a)}var mde={kernelName:qi,backendName:"webgpu",kernelFunc:fde},gde=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=lie(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},yde={kernelName:Gl,backendName:"webgpu",kernelFunc:gde},X8=Jt({opType:Pe.DIV}),xde={kernelName:ci,backendName:"webgpu",kernelFunc:X8},Ade=et({opType:le.RECIPROCAL}),bde={kernelName:Xi,backendName:"webgpu",kernelFunc:Ade},vde=et({opType:le.RELU}),kde={kernelName:Ki,backendName:"webgpu",kernelFunc:vde},wde=et({opType:le.RELU6}),Ide={kernelName:Ji,backendName:"webgpu",kernelFunc:wde},Sde=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
       ${ke("index")} {
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -7131,7 +7131,7 @@ return a / b;`,jK=`
             setOutputAtIndex(index, outputValue);
           }
         }
-      `}},Fde={kernelName:go,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new Pde(n.shape,s),[u,p]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},Ode=et({opType:le.ROUND}),Dde={kernelName:eo,backendName:"webgpu",kernelFunc:Ode},zde=et({opType:le.RSQRT,cpuKernelImpl:uie}),Lde={kernelName:to,backendName:"webgpu",kernelFunc:zde},Sc=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=$e(e),this.dispatch=we(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}`;let l=oa(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=`
+      `}},Fde={kernelName:go,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new Pde(n.shape,s),[u,p]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},Ode=et({opType:le.ROUND}),Dde={kernelName:eo,backendName:"webgpu",kernelFunc:Ode},zde=et({opType:le.RSQRT,cpuKernelImpl:uie}),Lde={kernelName:to,backendName:"webgpu",kernelFunc:zde},Ic=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=$e(e),this.dispatch=we(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}`;let l=oa(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=`
       fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
         return index;
       }
@@ -7157,12 +7157,12 @@ return a / b;`,jK=`
             flattenedIndex = flattenedIndex + indexInside * ${a};
           }
           let updateValue =
-              ${ad(this.type,!1)}(${s});
+              ${td(this.type,!1)}(${s});
           let flatIndex = getOutputIndexFromCoords(${n});
 
           ${this.sumDupeIndices?O3("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast<i32>(updateValue));"}
         }
-      }`}};function Bde(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=Ie({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=Ie({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=f.dtype,g=Nr({backend:a,attrs:{shape:d,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),x=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[y]}],A=new Sc(f.shape,o,h.shape.length,f.shape.length,p,d,m),b=a.runWebGPUProgram(A,[f,h],m,x,g),k=Ie({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(f.dataId),a.disposeData(b.dataId),k}var Wde={kernelName:ao,backendName:"webgpu",kernelFunc:Bde},Vde=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`
+      }`}};function Bde(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=Ie({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=Ie({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=f.dtype,g=Nr({backend:a,attrs:{shape:d,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),x=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[y]}],A=new Ic(f.shape,o,h.shape.length,f.shape.length,p,d,m),b=a.runWebGPUProgram(A,[f,h],m,x,g),k=Ie({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(f.dataId),a.disposeData(b.dataId),k}var Wde={kernelName:ao,backendName:"webgpu",kernelFunc:Bde},Vde=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`
       fn findBound(batch: i32, value: f32) -> i32 {
         var left = i32(0);
         var right = uniforms.numInputs;
@@ -7184,7 +7184,7 @@ return a / b;`,jK=`
           setOutputAtIndexI32(index, findBound(coords[0], value));
         }
       }
-    `}};function Ude(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new Vde([s.shape[0],s.shape[1]],i),l=[{type:"int32",data:[r.shape[1]]}];return a.runWebGPUProgram(o,[r,s],"int32",l)}var Gde={kernelName:Ld,backendName:"webgpu",kernelFunc:Ude},Hde=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=a,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s<this.outputShape.length;s++)r.push(`${a[s]}`),s<this.cRank&&n.push(`${a[s]}`);e=n.join(),t=r.join()}return`
+    `}};function Ude(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new Vde([s.shape[0],s.shape[1]],i),l=[{type:"int32",data:[r.shape[1]]}];return a.runWebGPUProgram(o,[r,s],"int32",l)}var Gde={kernelName:zd,backendName:"webgpu",kernelFunc:Ude},Hde=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=a,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s<this.outputShape.length;s++)r.push(`${a[s]}`),s<this.cRank&&n.push(`${a[s]}`);e=n.join(),t=r.join()}return`
       ${ke("index")} {
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -7196,14 +7196,14 @@ return a / b;`,jK=`
           }
         }
       }
-    `}};function jde(e){let{inputs:t,backend:a}=e,{condition:n,t:r,e:s}=t,i=new Hde(n.shape.length,r.shape,r.shape.length);return a.runWebGPUProgram(i,[n,r,s],fa(r.dtype,s.dtype))}var qde={kernelName:jl,backendName:"webgpu",kernelFunc:jde},Xde=et({opType:le.SELU}),Kde={kernelName:ql,backendName:"webgpu",kernelFunc:Xde},Zde=et({opType:le.SIGMOID}),Yde={kernelName:ro,backendName:"webgpu",kernelFunc:Zde},Jde=et({opType:le.SIGN}),Qde={kernelName:Zl,backendName:"webgpu",kernelFunc:Jde},epe=et({opType:le.SIN}),tpe={kernelName:no,backendName:"webgpu",kernelFunc:epe},ape=et({opType:le.SINH}),npe={kernelName:Kl,backendName:"webgpu",kernelFunc:ape},Z8=Jt({opType:Pe.SUB,cpuKernelImpl:mie,supportsComplex:!0}),rpe={kernelName:po,backendName:"webgpu",kernelFunc:Z8};function spe(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=V3({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=Ie({inputs:{x:o},backend:a,attrs:{shape:l}}),p=Z8({inputs:{a:r,b:u},backend:a}),c=U8({inputs:{x:p},backend:a}),d=U3({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=Ie({inputs:{x:d},backend:a,attrs:{shape:l}}),f=K8({inputs:{a:c,b:h},backend:a});return a.disposeData(o.dataId),a.disposeData(u.dataId),a.disposeData(p.dataId),a.disposeData(c.dataId),a.disposeData(d.dataId),a.disposeData(h.dataId),f}var ipe={kernelName:oo,backendName:"webgpu",kernelFunc:spe},ope=et({opType:le.SOFTPLUS}),lpe={kernelName:Yl,backendName:"webgpu",kernelFunc:ope},upe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let u=[],p=X8({inputs:{x:r},backend:a,attrs:{paddings:l,constantValue:0}}),c=T.getReshaped(p.shape,s,o,!1),d=T.getPermuted(c.length,s.length,!1),h=T.getReshapedPermuted(p.shape,s,o,!1),f=Ie({inputs:{x:p},backend:a,attrs:{shape:c}}),m=kr({inputs:{x:f},backend:a,attrs:{perm:d}}),g=Ie({inputs:{x:m},backend:a,attrs:{shape:h}});return u.push(p),u.push(f),u.push(m),u.forEach(y=>a.disposeData(y.dataId)),g},dpe={kernelName:Jl,backendName:"webgpu",kernelFunc:upe},ppe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n<a.length;n++)a[n]=e[n]*t[n];this.outputShape=a,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=cpe(this.rank,"uniforms.");return`
+    `}};function jde(e){let{inputs:t,backend:a}=e,{condition:n,t:r,e:s}=t,i=new Hde(n.shape.length,r.shape,r.shape.length);return a.runWebGPUProgram(i,[n,r,s],fa(r.dtype,s.dtype))}var qde={kernelName:jl,backendName:"webgpu",kernelFunc:jde},Xde=et({opType:le.SELU}),Kde={kernelName:ql,backendName:"webgpu",kernelFunc:Xde},Zde=et({opType:le.SIGMOID}),Yde={kernelName:ro,backendName:"webgpu",kernelFunc:Zde},Jde=et({opType:le.SIGN}),Qde={kernelName:Zl,backendName:"webgpu",kernelFunc:Jde},epe=et({opType:le.SIN}),tpe={kernelName:no,backendName:"webgpu",kernelFunc:epe},ape=et({opType:le.SINH}),npe={kernelName:Kl,backendName:"webgpu",kernelFunc:ape},K8=Jt({opType:Pe.SUB,cpuKernelImpl:mie,supportsComplex:!0}),rpe={kernelName:po,backendName:"webgpu",kernelFunc:K8};function spe(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=V3({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=Ie({inputs:{x:o},backend:a,attrs:{shape:l}}),p=K8({inputs:{a:r,b:u},backend:a}),c=V8({inputs:{x:p},backend:a}),d=U3({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=Ie({inputs:{x:d},backend:a,attrs:{shape:l}}),f=X8({inputs:{a:c,b:h},backend:a});return a.disposeData(o.dataId),a.disposeData(u.dataId),a.disposeData(p.dataId),a.disposeData(c.dataId),a.disposeData(d.dataId),a.disposeData(h.dataId),f}var ipe={kernelName:oo,backendName:"webgpu",kernelFunc:spe},ope=et({opType:le.SOFTPLUS}),lpe={kernelName:Yl,backendName:"webgpu",kernelFunc:ope},upe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let u=[],p=q8({inputs:{x:r},backend:a,attrs:{paddings:l,constantValue:0}}),c=T.getReshaped(p.shape,s,o,!1),d=T.getPermuted(c.length,s.length,!1),h=T.getReshapedPermuted(p.shape,s,o,!1),f=Ie({inputs:{x:p},backend:a,attrs:{shape:c}}),m=kr({inputs:{x:f},backend:a,attrs:{perm:d}}),g=Ie({inputs:{x:m},backend:a,attrs:{shape:h}});return u.push(p),u.push(f),u.push(m),u.forEach(y=>a.disposeData(y.dataId)),g},dpe={kernelName:Jl,backendName:"webgpu",kernelFunc:upe},ppe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n<a.length;n++)a[n]=e[n]*t[n];this.outputShape=a,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=cpe(this.rank,"uniforms.");return`
       ${ke("index")} {
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function cpe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r<e;r++)n.push(`(${a[r]} % ${t}aShape[${r}])`);return n.join()}function Y8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reps:s}=n;if(a.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=gie(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new ppe(r.shape,s);return a.runWebGPUProgram(i,[r],r.dtype)}var hpe={kernelName:rs,backendName:"webgpu",kernelFunc:Y8};function fpe(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let N=a.bufferSync(r),$=a.bufferSync(s),M=v.decodeString(a.readSync(i.dataId)[0]),R=die(N,$,o,d,p,u,l,c,M,h);return a.makeTensorInfo(o,R.dtype,R.values)}let f=[d/p,p],m=Ie({inputs:{x:r},backend:a,attrs:{shape:[u,l]}}),g=s.shape.length?Ie({inputs:{x:s},backend:a,attrs:{shape:[u,p]}}):Ja({inputs:{x:s},backend:a}),y=g.dtype,x=a.makeTensorInfo([],y,v.makeZerosTypedArray(1,y)),A=Ie({inputs:{x:i},backend:a,attrs:{shape:Array(f.length).fill(1)}}),b=Y8({inputs:{x:A},backend:a,attrs:{reps:f}}),k=v.sizeFromShape([u,p]),S=[{type:"int32",data:[l]},{type:"int32",data:c},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let N=new Sc([u,p],l,m.shape.length,g.shape.length,c,f,y,h);a.runWebGPUProgram(N,[g,m],y,S,b)}break;default:{let N=new Sc([u,p],l,m.shape.length,x.shape.length,c,f,y,h);a.runWebGPUProgram(N,[x,m],y,S,b)}{let N=new Sc([u,p],l,m.shape.length,g.shape.length,c,f,y);a.runWebGPUProgram(N,[g,m],y,S,b)}}let C=Ie({inputs:{x:b},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),a.disposeData(g.dataId),a.disposeData(A.dataId),a.disposeData(x.dataId),a.disposeData(b.dataId),C}var mpe={kernelName:Ud,backendName:"webgpu",kernelFunc:fpe};function gpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let f=Au({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,f})}var ype={kernelName:Ql,backendName:"webgpu",kernelFunc:gpe},xpe=et({opType:le.SQRT}),Ape={kernelName:so,backendName:"webgpu",kernelFunc:xpe},bpe={kernelName:Gd,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new xu(a.shape,le.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},vpe=Jt({opType:Pe.SQUARED_DIFFERENCE}),kpe={kernelName:lo,backendName:"webgpu",kernelFunc:vpe};function wpe({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=new xu(n.shape,le.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return a.runWebGPUProgram(r,[n],n.dtype,s)}var Ipe={kernelName:ss,backendName:"webgpu",kernelFunc:wpe},Spe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=oa(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return`
+    `}};function cpe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r<e;r++)n.push(`(${a[r]} % ${t}aShape[${r}])`);return n.join()}function Z8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reps:s}=n;if(a.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=gie(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new ppe(r.shape,s);return a.runWebGPUProgram(i,[r],r.dtype)}var hpe={kernelName:rs,backendName:"webgpu",kernelFunc:Z8};function fpe(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let N=a.bufferSync(r),$=a.bufferSync(s),M=v.decodeString(a.readSync(i.dataId)[0]),R=die(N,$,o,d,p,u,l,c,M,h);return a.makeTensorInfo(o,R.dtype,R.values)}let f=[d/p,p],m=Ie({inputs:{x:r},backend:a,attrs:{shape:[u,l]}}),g=s.shape.length?Ie({inputs:{x:s},backend:a,attrs:{shape:[u,p]}}):Ja({inputs:{x:s},backend:a}),y=g.dtype,x=a.makeTensorInfo([],y,v.makeZerosTypedArray(1,y)),A=Ie({inputs:{x:i},backend:a,attrs:{shape:Array(f.length).fill(1)}}),b=Z8({inputs:{x:A},backend:a,attrs:{reps:f}}),k=v.sizeFromShape([u,p]),S=[{type:"int32",data:[l]},{type:"int32",data:c},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let N=new Ic([u,p],l,m.shape.length,g.shape.length,c,f,y,h);a.runWebGPUProgram(N,[g,m],y,S,b)}break;default:{let N=new Ic([u,p],l,m.shape.length,x.shape.length,c,f,y,h);a.runWebGPUProgram(N,[x,m],y,S,b)}{let N=new Ic([u,p],l,m.shape.length,g.shape.length,c,f,y);a.runWebGPUProgram(N,[g,m],y,S,b)}}let C=Ie({inputs:{x:b},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),a.disposeData(g.dataId),a.disposeData(A.dataId),a.disposeData(x.dataId),a.disposeData(b.dataId),C}var mpe={kernelName:Vd,backendName:"webgpu",kernelFunc:fpe};function gpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let f=Au({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,f})}var ype={kernelName:Ql,backendName:"webgpu",kernelFunc:gpe},xpe=et({opType:le.SQRT}),Ape={kernelName:so,backendName:"webgpu",kernelFunc:xpe},bpe={kernelName:Ud,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new xu(a.shape,le.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},vpe=Jt({opType:Pe.SQUARED_DIFFERENCE}),kpe={kernelName:lo,backendName:"webgpu",kernelFunc:vpe};function wpe({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=new xu(n.shape,le.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return a.runWebGPUProgram(r,[n],n.dtype,s)}var Ipe={kernelName:ss,backendName:"webgpu",kernelFunc:wpe},Spe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=oa(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return`
        ${ke("index")} {
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
@@ -7337,7 +7337,7 @@ return a / b;`,jK=`
             }
           }
         }
-      `}};function Yo(e,t){t!==null&&e.disposeData(t.dataId)}function Jy(e){let t=1;for(;t<e;)t*=2;return t}function Ope(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{k:s,sorted:i}=n,o=r.shape,l=o[o.length-1];if(a.shouldExecuteOnCPU([r])){let b=a.readSync(r.dataId),[k,S]=yie(b,o,r.dtype,s,i);return[a.makeTensorInfo(k.shape,k.dtype,k.values),a.makeTensorInfo(S.shape,S.dtype,S.values)]}if(s===0)return o[o.length-1]=0,[a.makeTensorInfo(o,r.dtype,[]),a.makeTensorInfo(o,"int32",[])];if(l===1)return[r,Nr({attrs:{shape:o,dtype:"int32",value:0},backend:a})];let u=v.sizeFromShape(o)/l,p=Ie({inputs:{x:r},attrs:{shape:[u,l]},backend:a}),c=Jy(s),d=Jy(l),h=null,f=()=>h===null?[p,p]:[p,h],m=(b,k,S)=>{let C=f(),N=new Ppe(S),$=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[k]}],M=h;h=a.runWebGPUProgram(N,C,"int32",$),Yo(a,M)};for(let b=1;b<c;b*=2){let k=b*2;for(let S=b;S>=1;S/=2)m(k,S,[u,d])}for(let b=d;b>c;b/=2){let k=f(),S=new Fpe([u,b/2]),C=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"int32",data:[c]}],N=h;h=a.runWebGPUProgram(S,k,"int32",C),Yo(a,N);let $=c/2,M=$*2;for(let R=$;R>=1;R/=2)m(M,R,h.shape)}let g=h;h=Au({inputs:{x:h},backend:a,attrs:{begin:0,size:[u,s]}}),Yo(a,g);let y=H8({inputs:{x:p,indices:h},backend:a,attrs:{axis:1,batchDims:1}});Yo(a,p);let x=o.slice(0,-1);x.push(s),g=h,h=Ie({inputs:{x:h},attrs:{shape:x},backend:a}),Yo(a,g);let A=y;return y=Ie({inputs:{x:y},attrs:{shape:x},backend:a}),Yo(a,A),[y,h]}var Dpe={kernelName:fo,backendName:"webgpu",kernelFunc:Ope},zpe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function Yo(e,t){t!==null&&e.disposeData(t.dataId)}function Yy(e){let t=1;for(;t<e;)t*=2;return t}function Ope(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{k:s,sorted:i}=n,o=r.shape,l=o[o.length-1];if(a.shouldExecuteOnCPU([r])){let b=a.readSync(r.dataId),[k,S]=yie(b,o,r.dtype,s,i);return[a.makeTensorInfo(k.shape,k.dtype,k.values),a.makeTensorInfo(S.shape,S.dtype,S.values)]}if(s===0)return o[o.length-1]=0,[a.makeTensorInfo(o,r.dtype,[]),a.makeTensorInfo(o,"int32",[])];if(l===1)return[r,Nr({attrs:{shape:o,dtype:"int32",value:0},backend:a})];let u=v.sizeFromShape(o)/l,p=Ie({inputs:{x:r},attrs:{shape:[u,l]},backend:a}),c=Yy(s),d=Yy(l),h=null,f=()=>h===null?[p,p]:[p,h],m=(b,k,S)=>{let C=f(),N=new Ppe(S),$=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[k]}],M=h;h=a.runWebGPUProgram(N,C,"int32",$),Yo(a,M)};for(let b=1;b<c;b*=2){let k=b*2;for(let S=b;S>=1;S/=2)m(k,S,[u,d])}for(let b=d;b>c;b/=2){let k=f(),S=new Fpe([u,b/2]),C=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"int32",data:[c]}],N=h;h=a.runWebGPUProgram(S,k,"int32",C),Yo(a,N);let $=c/2,M=$*2;for(let R=$;R>=1;R/=2)m(M,R,h.shape)}let g=h;h=Au({inputs:{x:h},backend:a,attrs:{begin:0,size:[u,s]}}),Yo(a,g);let y=G8({inputs:{x:p,indices:h},backend:a,attrs:{axis:1,batchDims:1}});Yo(a,p);let x=o.slice(0,-1);x.push(s),g=h,h=Ie({inputs:{x:h},attrs:{shape:x},backend:a}),Yo(a,g);let A=y;return y=Ie({inputs:{x:y},attrs:{shape:x},backend:a}),Yo(a,A),[y,h]}var Dpe={kernelName:fo,backendName:"webgpu",kernelFunc:Ope},zpe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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) {
@@ -7453,7 +7453,7 @@ return a / b;`,jK=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function Lpe(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,c,d,h]=r.shape,[f,m]=u!=null?u:[c,d],g=[p,f,m,h],y=new zpe(g),x=i==="nearest"?1:2,A;switch(o){case"constant":A=1;break;case"reflect":A=2;break;case"wrap":A=3;break;case"nearest":A=4;break;default:A=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[A]},{type:"float32",data:[l]}];return a.runWebGPUProgram(y,[r,s],"float32",b)}var Bpe={kernelName:mo,backendName:"webgpu",kernelFunc:Lpe};function Wpe(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let m=0;m<o;m++)m!==s&&(u[p++]=i.shape[m]);let c=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let g=Au({inputs:{x:i},backend:a,attrs:{begin:d,size:h}}),y=Ie({inputs:{x:g},backend:a,attrs:{shape:u}});f[m]=y,c.push(g)}return c.forEach(m=>a.disposeData(m.dataId)),f}var Vpe={kernelName:au,backendName:"webgpu",kernelFunc:Wpe},Upe=[Dse,bie,kie,Iie,Tie,Eie,Oie,zie,Bie,Vie,Gie,jie,Xie,Zie,Jie,noe,ioe,loe,hoe,yoe,voe,woe,Coe,Lse,Moe,Loe,Uoe,qoe,Koe,Yoe,ele,ale,rle,ile,ule,cle,mle,xle,kle,Ile,Tle,Nle,Ele,Rle,$le,Ple,Pse,Ole,Vle,zle,Ble,Hle,qle,Kle,Jle,tue,nue,sue,zse,oue,Eoe,uue,pue,hue,mue,yue,Aue,kue,Tue,Iue,Nue,Rue,$ue,Oue,eoe,zue,Bue,toe,Vue,Gue,jue,Xue,Ale,Zue,Jue,ede,xoe,nde,sde,ode,ude,pde,hde,mde,yde,Aoe,xde,bde,kde,Ide,Fse,Cde,Rde,_de,Fde,Dde,Lde,Wde,Gde,qde,Kde,Yde,Qde,tpe,npe,poe,Ipe,Cpe,Epe,ipe,lpe,dpe,mpe,ype,Ape,bpe,kpe,rpe,ble,Mpe,_pe,hpe,Dpe,Bpe,_ie,Vpe,rde];for(let e of Upe)gn(e);var Qy="4.2.0",Gpe="4.2.0",Hpe="4.2.0",jpe="4.2.0",qpe="4.2.0",Xpe="0.0.1-alpha.17",Ap={tfjs:Qy,"tfjs-core":Qy,"tfjs-converter":Gpe,"tfjs-backend-cpu":Hpe,"tfjs-backend-webgl":jpe,"tfjs-backend-wasm":qpe,"tfjs-backend-webgpu":Xpe};function K(...e){let t=new Date,a=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(a,"Human:",...e)}function J8(e,t){let a=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${a}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var ae=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function G3(e,t,a="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")G3(e[r],t[r],r,n);else{let s=e&&typeof e[r]!="undefined";s||n.push({reason:"unknown property",where:`${a}.${r} = ${t[r]}`});let i=e&&typeof e[r]==typeof t[r];s&&!i&&n.push({reason:"property type mismatch",where:`${a}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&a==="config"&&n.length>0&&K("invalid configuration",n),n}function Nt(...e){let t=a=>a&&typeof a=="object";return e.reduce((a,n)=>(Object.keys(n||{}).forEach(r=>{let s=a[r],i=n[r];Array.isArray(s)&&Array.isArray(i)?a[r]=s.concat(...i):t(s)&&t(i)?a[r]=Nt(s,i):a[r]=i}),a),{})}var So={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var Q8=`
+        `}};function Lpe(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,c,d,h]=r.shape,[f,m]=u!=null?u:[c,d],g=[p,f,m,h],y=new zpe(g),x=i==="nearest"?1:2,A;switch(o){case"constant":A=1;break;case"reflect":A=2;break;case"wrap":A=3;break;case"nearest":A=4;break;default:A=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[A]},{type:"float32",data:[l]}];return a.runWebGPUProgram(y,[r,s],"float32",b)}var Bpe={kernelName:mo,backendName:"webgpu",kernelFunc:Lpe};function Wpe(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let m=0;m<o;m++)m!==s&&(u[p++]=i.shape[m]);let c=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let g=Au({inputs:{x:i},backend:a,attrs:{begin:d,size:h}}),y=Ie({inputs:{x:g},backend:a,attrs:{shape:u}});f[m]=y,c.push(g)}return c.forEach(m=>a.disposeData(m.dataId)),f}var Vpe={kernelName:au,backendName:"webgpu",kernelFunc:Wpe},Upe=[Dse,bie,kie,Iie,Tie,Eie,Oie,zie,Bie,Vie,Gie,jie,Xie,Zie,Jie,noe,ioe,loe,hoe,yoe,voe,woe,Coe,Lse,Moe,Loe,Uoe,qoe,Koe,Yoe,ele,ale,rle,ile,ule,cle,mle,xle,kle,Ile,Tle,Nle,Ele,Rle,$le,Ple,Pse,Ole,Vle,zle,Ble,Hle,qle,Kle,Jle,tue,nue,sue,zse,oue,Eoe,uue,pue,hue,mue,yue,Aue,kue,Tue,Iue,Nue,Rue,$ue,Oue,eoe,zue,Bue,toe,Vue,Gue,jue,Xue,Ale,Zue,Jue,ede,xoe,nde,sde,ode,ude,pde,hde,mde,yde,Aoe,xde,bde,kde,Ide,Fse,Cde,Rde,_de,Fde,Dde,Lde,Wde,Gde,qde,Kde,Yde,Qde,tpe,npe,poe,Ipe,Cpe,Epe,ipe,lpe,dpe,mpe,ype,Ape,bpe,kpe,rpe,ble,Mpe,_pe,hpe,Dpe,Bpe,_ie,Vpe,rde];for(let e of Upe)gn(e);var Jy="4.2.0",Gpe="4.2.0",Hpe="4.2.0",jpe="4.2.0",qpe="4.2.0",Xpe="0.0.1-alpha.17",xp={tfjs:Jy,"tfjs-core":Jy,"tfjs-converter":Gpe,"tfjs-backend-cpu":Hpe,"tfjs-backend-webgl":jpe,"tfjs-backend-wasm":qpe,"tfjs-backend-webgpu":Xpe};function K(...e){let t=new Date,a=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(a,"Human:",...e)}function Y8(e,t){let a=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${a}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var ae=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function G3(e,t,a="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")G3(e[r],t[r],r,n);else{let s=e&&typeof e[r]!="undefined";s||n.push({reason:"unknown property",where:`${a}.${r} = ${t[r]}`});let i=e&&typeof e[r]==typeof t[r];s&&!i&&n.push({reason:"property type mismatch",where:`${a}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&a==="config"&&n.length>0&&K("invalid configuration",n),n}function Nt(...e){let t=a=>a&&typeof a=="object";return e.reduce((a,n)=>(Object.keys(n||{}).forEach(r=>{let s=a[r],i=n[r];Array.isArray(s)&&Array.isArray(i)?a[r]=s.concat(...i):t(s)&&t(i)?a[r]=Nt(s,i):a[r]=i}),a),{})}var So={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var J8=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -7463,7 +7463,7 @@ return a / b;`,jK=`
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var e9=`
+`;var Q8=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -7475,7 +7475,7 @@ return a / b;`,jK=`
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,t9=`
+`,e9=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -7487,7 +7487,7 @@ return a / b;`,jK=`
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,a9=`
+`,t9=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -7500,7 +7500,7 @@ return a / b;`,jK=`
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,n9=`
+`,a9=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -7523,7 +7523,7 @@ return a / b;`,jK=`
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,r9=`
+`,n9=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -7545,8 +7545,8 @@ return a / b;`,jK=`
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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khe=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],whe=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],Ihe=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],She=[[474,475],[475,476],[476,477],[477,474]],The=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],Che=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Nhe=[[469,470],[470,471],[471,472],[472,469]],Ehe=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function cs(e){let t=e.map(a=>a[0]);return t.push(e[e.length-1][1]),t}var Rhe={lips:cs(khe),leftEye:cs(whe),leftEyebrow:cs(Ihe),leftIris:cs(She),rightEye:cs(The),rightEyebrow:cs(Che),rightIris:cs(Nhe),faceOval:cs(Ehe)},Mhe=Object.entries(Rhe).map(([e,t])=>t.map(a=>[a,e])).flat(),N3e=new 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     [gender] [genderScore]%
     age: [age] years
@@ -7567,7 +7567,7 @@ return a / b;`,jK=`
     live: [live]%
     [emotions]
     roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0
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pg=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],cg={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Et,E9=0,Ma={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},hg=Number.MAX_SAFE_INTEGER;async function R9(e){return re.initial&&(Et=null),Et?e.debug&&K("cached model:",Et.modelUrl):Et=await Me(e.body.modelPath),Et}async function Yhe(e,t){let[a,n]=e.shape,r=Q(e,[n*a]),s=ha(r,0),i=(await s.data())[0];if(i>t){let o=ar(r,0),l=su(o,a),u=(await l.data())[0],p=xe(o,a),c=(await p.data())[0];return J([r,s,o,l,p]),[u,c,i]}return J([r,s]),[0,0,i]}async function fg(e,t){if(!(Et!=null&&Et.executor)||!(Et!=null&&Et.inputs[0].shape))return[];let a=(t.body.skipTime||0)>ae()-E9,n=hg<(t.body.skipFrames||0);return t.skipAllowed&&a&&n&&Object.keys(Ma.keypoints).length>0?(hg++,[Ma]):(hg=0,new Promise(async r=>{let s=Fe(()=>{var f,m;let c=ye.resizeBilinear(e,[((f=Et==null?void 0:Et.inputs[0].shape)==null?void 0:f[2])||0,((m=Et==null?void 0:Et.inputs[0].shape)==null?void 0:m[1])||0],!1),d=te(c,Le.tf2);return ge(d,Le.tf1)}),i;if(t.body.enabled&&(i=Et==null?void 0:Et.execute(s)),E9=ae(),J(s),i){Ma.keypoints.length=0;let c=Oe(i);J(i);let d=Ca(c,2);J(c);for(let h=0;h<d.length;h++){let[f,m,g]=await Yhe(d[h],t.body.minConfidence);g>(t.body.minConfidence||0)&&Ma.keypoints.push({score:Math.round(100*g)/100,part:pg[h],positionRaw:[f/Et.inputs[0].shape[2],m/Et.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/Et.inputs[0].shape[2]),Math.round(e.shape[1]*m/Et.inputs[0].shape[1])]})}d.forEach(h=>J(h))}Ma.score=Ma.keypoints.reduce((c,d)=>d.score>c?d.score:c,0);let o=Ma.keypoints.map(c=>c.position[0]),l=Ma.keypoints.map(c=>c.position[1]);Ma.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Ma.keypoints.map(c=>c.positionRaw[0]),p=Ma.keypoints.map(c=>c.positionRaw[1]);Ma.boxRaw=[Math.min(...u),Math.min(...p),Math.max(...u)-Math.min(...u),Math.max(...p)-Math.min(...p)];for(let[c,d]of Object.entries(cg)){let h=[];for(let f=0;f<d.length-1;f++){let m=Ma.keypoints.find(y=>y.part===d[f]),g=Ma.keypoints.find(y=>y.part===d[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}Ma.annotations[c]=h}r([Ma])}))}var ku=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],l0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],u0=(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],d0=(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],P9=(e,t)=>{let 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t=e.map(n=>n[0]),a=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...a)],endPoint:[Math.max(...t),Math.max(...a)],landmarks:e}},gg=[[1,0,0],[0,1,0],[0,0,1]],Jhe=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),Qhe=(e,t)=>Jhe(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var $9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Po=(e,t)=>{let a=0;for(let n=0;n<e.length;n++)a+=e[n]*t[n];return a},e0e=(e,t)=>{let a=[];for(let n=0;n<e.length;n++)a.push(e[n][t]);return a},_9=(e,t)=>{let a=[],n=e.length;for(let r=0;r<n;r++){a.push([]);for(let s=0;s<n;s++)a[r].push(Po(e[r],e0e(t,s)))}return a},O9=(e,t)=>{let a=Math.cos(e),n=Math.sin(e),r=[[a,-n,0],[n,a,0],[0,0,1]],s=$9(t[0],t[1]),i=_9(s,r),o=$9(-t[0],-t[1]);return _9(i,o)},t0e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],a=[e[0][2],e[1][2]],n=[-Po(t[0],a),-Po(t[1],a)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},a0e=(e,t)=>[Po(e,t[0]),Po(e,t[1])];function D9(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},a=[];for(let n=0;n<t.strides.length;n++){let r=t.strides[n],s=Math.floor((e+r-1)/r),i=Math.floor((e+r-1)/r),o=t.anchors[n];for(let l=0;l<s;l++){let u=r*(l+.5);for(let p=0;p<i;p++){let c=r*(p+.5);for(let d=0;d<o;d++)a.push([c,u])}}}return a}function z9(e,t,a,n,r){let s=ku(t),i=e.map(h=>[s[0]/r*(h[0]-r/2),s[1]/r*(h[1]-r/2),h[2]||0]),o=a&&a!==0&&Math.abs(a)>.2,l=o?O9(a,[0,0]):gg,u=o?i.map(h=>[...a0e(h,l),h[2]]):i,p=o?t0e(n):gg,c=l0(t),d=[Po(c,p[0]),Po(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2]||0)])}function L9(e,t,a,n){let r=t.landmarks.length>=J3.count?J3.symmetryLine:No.symmetryLine,s=0,i=gg,o;if(e&&re.kernels.includes("rotatewithoffset"))if(s=Qhe(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let u=l0(t),p=[u[0]/a.shape[2],u[1]/a.shape[1]],c=ye.rotateWithOffset(a,s,0,[p[0],p[1]]);i=O9(-s,u),o=mg(t,c,[n,n]),J(c)}else o=mg(t,a,[n,n]);else o=mg(t,a,[n,n]);return[s,i,o]}var n0e=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...a)+(Math.max(...a)-Math.min(...a))/2]},B9=(e,t)=>{let a=n0e(e),n=ku(t);return{startPoint:[a[0]-n[0]/2,a[1]-n[1]/2],endPoint:[a[0]+n[0]/2,a[1]+n[1]/2]}};var W9=6,r0e=1.4,Ln,xg=null,ms=0,wu=null,Iu=()=>ms;async function V9(e){var t;return re.initial&&(Ln=null),Ln?e.debug&&K("cached model:",Ln.modelUrl):Ln=await Me((t=e.face.detector)==null?void 0:t.modelPath),ms=Ln.executor&&Ln.inputs[0].shape?Ln.inputs[0].shape[2]:256,wu=ze(ms,"int32"),xg=Kn(D9(ms)),Ln}function s0e(e){if(!xg||!wu)return mn([0,0]);let t={};t.boxStarts=De(e,[0,1],[-1,2]),t.centers=be(t.boxStarts,xg),t.boxSizes=De(e,[0,3],[-1,2]),t.boxSizesNormalized=xe(t.boxSizes,wu),t.centersNormalized=xe(t.centers,wu),t.halfBoxSize=xe(t.boxSizesNormalized,Le.tf2),t.starts=ge(t.centersNormalized,t.halfBoxSize),t.ends=be(t.centersNormalized,t.halfBoxSize),t.startNormalized=te(t.starts,wu),t.endNormalized=te(t.ends,wu);let a=ru([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>J(t[n])),a}async function U9(e,t){var o,l,u,p,c,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let a={};a.resized=ye.resizeBilinear(e,[ms,ms]),a.div=xe(a.resized,Le.tf127),a.normalized=ge(a.div,Le.tf05);let n=Ln==null?void 0:Ln.execute(a.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((f,m)=>f.size-m.size);a.concat384=st([h[0],h[2]],2),a.concat512=st([h[1],h[3]],2),a.concat=st([a.concat512,a.concat384],1),a.batch=Oe(a.concat,[0])}else Array.isArray(n)?a.batch=Oe(n[0]):a.batch=Oe(n);J(n),a.boxes=s0e(a.batch),a.logits=De(a.batch,[0,0],[-1,1]),a.sigmoid=za(a.logits),a.scores=Oe(a.sigmoid),a.nms=await ye.nonMaxSuppressionAsync(a.boxes,a.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 r=await a.nms.array(),s=[],i=await a.scores.data();for(let h=0;h<r.length;h++){let f=i[r[h]];if(f>(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let m={};m.bbox=De(a.boxes,[r[h],0],[1,-1]),m.slice=De(a.batch,[r[h],W9-1],[1,-1]),m.squeeze=Oe(m.slice),m.landmarks=Q(m.squeeze,[W9,-1]);let g=await m.bbox.data(),y={startPoint:[g[0],g[1]],endPoint:[g[2],g[3]],landmarks:await m.landmarks.array(),confidence:f},x=P9(y,[(e.shape[2]||0)/ms,(e.shape[1]||0)/ms]),A=p0(x,t.face.scale||r0e),b=c0(A);b.size[0]>(((c=t.face.detector)==null?void 0:c.minSize)||0)&&b.size[1]>(((d=t.face.detector)==null?void 0:d.minSize)||0)&&s.push(b),Object.keys(m).forEach(k=>J(m[k]))}}return Object.keys(a).forEach(h=>J(a[h])),s}var en,gs=0,i0e=2.3,Ag=Rn.leftEyeLower0,bg=Rn.rightEyeLower0,Su={leftBounds:[Ag[0],Ag[Ag.length-1]],rightBounds:[bg[0],bg[bg.length-1]]},Tu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function X9(e){var t,a;return re.initial&&(en=null),en?e.debug&&K("cached model:",en.modelUrl):en=await Me((t=e.face.iris)==null?void 0:t.modelPath),gs=en!=null&&en.executor&&((a=en.inputs)!=null&&a[0].shape)?en.inputs[0].shape[2]:0,gs===-1&&(gs=64),en}function h0(e,t,a,n){for(let r=0;r<Q3.length;r++){let{key:s,indices:i}=Q3[r],o=Rn[`${a}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var o0e=e=>{let t=e[Su.leftBounds[0]][2],a=e[Su.rightBounds[0]][2];return t-a},H9=(e,t,a,n,r,s=!1)=>{let i=c0(p0(F9([e[a],e[n]]),i0e)),o=ku(i),l=ye.cropAndResize(t,[[i.startPoint[1]/r,i.startPoint[0]/r,i.endPoint[1]/r,i.endPoint[0]/r]],[0],[gs,gs]);if(s&&re.kernels.includes("flipleftright")){let u=ye.flipLeftRight(l);J(l),l=u}return{box:i,boxSize:o,crop:l}},j9=(e,t,a,n=!1)=>{let r=[];for(let s=0;s<Tu.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];r.push([(n?1-i/gs:i/gs)*a[0]+t.startPoint[0],o/gs*a[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(Tu.index)}},q9=(e,t,a)=>{let n=e[Rn[`${a}EyeUpper0`][Tu.upperCenter]][2],r=e[Rn[`${a}EyeLower0`][Tu.lowerCenter]][2],s=(n+r)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=r),[i[0],i[1],l]})};async function K9(e,t,a){if(!(en!=null&&en.executor))return e;let{box:n,boxSize:r,crop:s}=H9(e,t,Su.leftBounds[0],Su.leftBounds[1],a,!0),{box:i,boxSize:o,crop:l}=H9(e,t,Su.rightBounds[0],Su.rightBounds[1],a,!0),u=st([s,l]);J(s),J(l);let p=en.execute(u);J(u);let c=await p.data();J(p);let d=c.slice(0,Tu.numCoordinates*3),{rawCoords:h,iris:f}=j9(d,n,r,!0),m=c.slice(Tu.numCoordinates*3),{rawCoords:g,iris:y}=j9(m,i,o,!1),x=o0e(e);Math.abs(x)<30?(h0(e,h,"left",null),h0(e,g,"right",null)):x<1?h0(e,h,"left",["EyeUpper0","EyeLower0"]):h0(e,g,"right",["EyeUpper0","EyeLower0"]);let A=q9(e,f,"left"),b=q9(e,y,"right");return e.concat(A).concat(b)}async function Y9(e,t){var s,i,o,l,u,p,c,d,h,f;let a={lips:await((i=(s=t.filter(m=>m.size===160))==null?void 0:s[0])==null?void 0:i.data()),irisL:await((l=(o=t.filter(m=>m.size===10))==null?void 0:o[0])==null?void 0:l.data()),eyeL:await((p=(u=t.filter(m=>m.size===142))==null?void 0:u[0])==null?void 0:p.data()),irisR:await((d=(c=t.filter(m=>m.size===10))==null?void 0:c[1])==null?void 0:d.data()),eyeR:await((f=(h=t.filter(m=>m.size===142))==null?void 0:h[1])==null?void 0:f.data())};for(let m of Object.values(a))if(!m)return e;let n=Ro.reduce((m,g)=>m+=e[g][2],0)/Ro.length;for(let m=0;m<a.irisL.length/2;m++)e.push([a.irisL[2*m+0],a.irisL[2*m+1],n]);let r=Mo.reduce((m,g)=>m+=e[g][2],0)/Mo.length;for(let m=0;m<a.irisR.length/2;m++)e.push([a.irisR[2*m+0],a.irisR[2*m+1],r]);for(let m=0;m<a.eyeL.length/2;m++)e[Ro[m]]=[a.eyeL[2*m+0],a.eyeL[2*m+1],e[Ro[m]][2]];for(let m=0;m<a.eyeR.length/2;m++)e[Mo[m]]=[a.eyeR[2*m+0],a.eyeR[2*m+1],e[Mo[m]][2]];for(let m=0;m<a.lips.length/2;m++)e[Tp[m]]=[a.lips[2*m+0],a.lips[2*m+1],e[Tp[m]][2]];return e}var or={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},wt=null,Cp=0;async function J9(e,t){var l,u,p,c,d,h,f,m,g,y;let a=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>ae()-or.timestamp,n=or.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!a||!n||or.boxes.length===0?(or.boxes=await U9(e,t),or.timestamp=ae(),or.skipped=0):or.skipped++;let r=[],s=[],i=0,o=Cp;for(let x=0;x<or.boxes.length;x++){let A=or.boxes[x],b=0,k,S={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([b,k,S.tensor]=L9((p=t.face.detector)==null?void 0:p.rotation,A,e,(c=t.face.mesh)!=null&&c.enabled?Cp:Iu()),t.filter.equalization){let C=S.tensor?await Uh(S.tensor):void 0;J(S.tensor),C&&(S.tensor=C)}if(S.boxScore=Math.round(100*A.confidence)/100,!((d=t.face.mesh)!=null&&d.enabled)||!(wt!=null&&wt.executor)){S.box=u0(A,e),S.boxRaw=d0(A,e),S.score=S.boxScore,S.size=A.size,S.mesh=A.landmarks.map(C=>[(A.startPoint[0]+A.endPoint[0])/2+C[0]*e.shape[2]/Iu(),(A.startPoint[1]+A.endPoint[1])/2+C[1]*e.shape[1]/Iu()]),S.meshRaw=S.mesh.map(C=>[C[0]/(e.shape[2]||0),C[1]/(e.shape[1]||0),(C[2]||0)/o]);for(let C of Object.keys(No))S.annotations[C]=[S.mesh[No[C]]]}else if(!wt)t.debug&&K("face mesh detection requested, but model is not loaded");else{if((h=t.face.attention)!=null&&h.enabled&&!re.kernels.includes("atan2"))return t.face.attention.enabled=!1,J(S.tensor),r;let C=wt.execute(S.tensor),$=await C.find(M=>M.shape[M.shape.length-1]===1).data();if(S.faceScore=Math.round(100*$[0])/100,S.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(A.confidence=S.faceScore,t.face.mesh.keepInvalid){S.box=u0(A,e),S.boxRaw=d0(A,e),S.score=S.boxScore,S.mesh=A.landmarks.map(M=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*M[0]/Iu(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*M[1]/Iu()]),S.meshRaw=S.mesh.map(M=>[M[0]/(e.shape[2]||1),M[1]/(e.shape[1]||1),(M[2]||0)/o]);for(let M of Object.keys(No))S.annotations[M]=[S.mesh[No[M]]]}}else{let M=C.find(D=>D.shape[D.shape.length-1]===1404),R=Q(M,[-1,3]),I=await R.array();J(R),(m=t.face.attention)!=null&&m.enabled?I=await Y9(I,C):(g=t.face.iris)!=null&&g.enabled&&(I=await K9(I,S.tensor,Cp)),S.mesh=z9(I,A,b,k,Cp),S.meshRaw=S.mesh.map(D=>[D[0]/(e.shape[2]||0),D[1]/(e.shape[1]||0),(D[2]||0)/o]);for(let D of 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re.initial&&(ta=null),ta?e.debug&&K("cached model:",ta.modelUrl):(ta=await Me((t=e.face.emotion)==null?void 0:t.modelPath),Ig=((n=(a=ta==null?void 0:ta.inputs)==null?void 0:a[0].shape)==null?void 0:n[3])===3,Ig?wg=["angry","disgust","fear","happy","neutral","sad","surprise"]:wg=["angry","disgust","fear","happy","sad","surprise","neutral"]),ta}async function Sg(e,t,a,n){var i,o;if(!ta)return[];let r=kg<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>ae()-nk;return t.skipAllowed&&s&&r&&ak===n&&f0[a]&&f0[a].length>0?(kg++,f0[a]):(kg=0,new Promise(async l=>{var p;let u=[];if((p=t.face.emotion)!=null&&p.enabled){let c={},d=ta!=null&&ta.inputs[0].shape?ta.inputs[0].shape[2]:0;if(t.face.emotion.crop>0){let f=t.face.emotion.crop,m=[[f,f,1-f,1-f]];c.resize=ye.cropAndResize(e,m,[0],[d,d])}else c.resize=ye.resizeBilinear(e,[d,d],!1);Ig?(c.mul=te(c.resize,255),c.normalize=ge(c.mul,[103.939,116.779,123.68]),c.emotion=ta==null?void 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r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(ga!=null&&ga.executor))return r;let s=Tg<(((o=t.face.description)==null?void 0:o.skipFrames)||0),i=(((l=t.face.description)==null?void 0:l.skipTime)||0)>ae()-ik;return t.skipAllowed&&s&&i&&ok===n&&((u=ys==null?void 0:ys[a])==null?void 0:u.age)>0&&((p=ys==null?void 0:ys[a])==null?void 0:p.genderScore)>0?(Tg++,ys[a]):(Tg=0,new Promise(async c=>{var d;if((d=t.face.description)!=null&&d.enabled){let h=u0e(e),f=ga==null?void 0:ga.execute(h);ik=ae(),J(h);let g=await f.find(N=>N.shape[1]===1).data(),y=Math.trunc(200*Math.abs(g[0]-.5))/100;y>(t.face.description.minConfidence||0)&&(r.gender=g[0]<=.5?"female":"male",r.genderScore=Math.min(.99,y));let x=ar(f.find(N=>N.shape[1]===100),1),A=(await x.data())[0];J(x);let k=await f.find(N=>N.shape[1]===100).data();r.age=Math.round(k[A-1]>k[A+1]?10*A-100*k[A-1]:10*A+100*k[A+1])/10,(Number.isNaN(g[0])||Number.isNaN(k[0]))&&K("faceres error:",{model:ga,result:f});let S=f.find(N=>N.shape[1]===1024),C=S?await S.data():[];r.descriptor=Array.from(C),f.forEach(N=>J(N))}ys[a]=r,ok=n,c(r)}))}var Cu=.1,Ng=.5;function d0e(e,t,a){let n=!1,r=a.length-1;for(let s=0;s<a.length;r=s++)a[s].y>t!=a[r].y>t&&e<(a[r].x-a[s].x)*(t-a[s].y)/(a[r].y-a[s].y)+a[s].x&&(n=!n);return n}async function dk(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,a=e.tensor.shape[1]||0,n=await e.tensor.buffer(),r=[];for(let i of Rn.silhouette)r.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Cu&&Cu>0&&(r=r.map(i=>({x:i.x>.5?i.x+Cu:i.x-Cu,y:i.y>.5?i.y+Cu:i.y-Cu})));for(let i=0;i<t;i++)for(let o=0;o<a;o++)d0e(i/t,o/t,r)||(n.set(Ng*n.get(0,o,i,0),0,o,i,0),n.set(Ng*n.get(0,o,i,1),0,o,i,1),n.set(Ng*n.get(0,o,i,2),0,o,i,2));return n.toTensor()}var aa,m0=[],Eg=Number.MAX_SAFE_INTEGER,pk=0,ck=0;async function hk(e){var t;return re.initial&&(aa=null),aa?e.debug&&K("cached model:",aa.modelUrl):aa=await 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t.skipAllowed&&r&&s&&mk===n&&g0[a]?(Mg++,g0[a]):(Mg=0,new Promise(async l=>{let u=ye.resizeBilinear(e,[na!=null&&na.inputs[0].shape?na.inputs[0].shape[2]:0,na!=null&&na.inputs[0].shape?na.inputs[0].shape[1]:0],!1),p=na==null?void 0:na.execute(u),c=(await p.data())[0];g0[a]=Math.round(100*c)/100,mk=n,gk=ae(),J([u,p]),l(g0[a])}))}var Mn,_g=[],c0e=["white","black","asian","indian","other"],h0e=[15,23,28,35.5,45.5,55.5,65],Ak=0,bk=0,Pg=Number.MAX_SAFE_INTEGER;async function vk(e){var t;return re.initial&&(Mn=null),Mn?e.debug&&K("cached model:",Mn.modelUrl):Mn=await Me((t=e.face.gear)==null?void 0:t.modelPath),Mn}async function Fg(e,t,a,n){var i,o;if(!Mn)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=Pg<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>ae()-bk;return t.skipAllowed&&s&&r&&Ak===n&&_g[a]?(Pg++,_g[a]):(Pg=0,new Promise(async l=>{var y,x;if(!(Mn!=null&&Mn.inputs[0].shape))return;let u={},p=[[0,.1,.9,.9]];u.resize=ye.cropAndResize(e,p,[0],[Mn.inputs[0].shape[2],Mn.inputs[0].shape[1]]);let c={age:0,gender:"unknown",genderScore:0,race:[]};(y=t.face.gear)!=null&&y.enabled&&([u.age,u.gender,u.race]=Mn.execute(u.resize,["age_output","gender_output","race_output"]));let d=await u.gender.data();c.gender=d[0]>d[1]?"male":"female",c.genderScore=Math.round(100*(d[0]>d[1]?d[0]:d[1]))/100;let h=await u.race.data();for(let A=0;A<h.length;A++)h[A]>(((x=t.face.gear)==null?void 0:x.minConfidence)||.2)&&c.race.push({score:Math.round(100*h[A])/100,race:c0e[A]});c.race.sort((A,b)=>b.score-A.score);let m=Array.from(await u.age.data()).map((A,b)=>[h0e[b],A]).sort((A,b)=>b[1]-A[1]),g=m[0][0];for(let A=1;A<m.length;A++)g+=m[A][1]*(m[A][0]-g);c.age=Math.round(10*g)/10,Object.keys(u).forEach(A=>J(u[A])),_g[a]=c,Ak=n,bk=ae(),l(c)}))}var tn,y0=[],wk=0,Ik=0,Og=Number.MAX_SAFE_INTEGER;async function Sk(e){return re.initial&&(tn=null),tn?e.debug&&K("cached model:",tn.modelUrl):tn=await 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y=Math.abs(e[i].mesh[145][1]-e[i].annotations.rightEyeIris[0][1])/e[i].box[3],x=Math.abs(e[i].mesh[374][1]-e[i].annotations.leftEyeIris[0][1])/e[i].box[3];(x<.01||y<.01||x>.022||y>.022)&&(h=!1),(x<.01||y<.01)&&t.push({iris:i,gesture:"looking down"}),(x>.022||y>.022)&&t.push({iris:i,gesture:"looking up"}),h&&t.push({iris:i,gesture:"looking center"})}return t},ew=e=>{if(!e)return[];let t=[];for(let a=0;a<e.length;a++){let n=[];if(e[a].annotations)for(let[r,s]of Object.entries(e[a].annotations))r!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:r.toLowerCase(),position:s[0]});if(n&&n.length>0){let r=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:a,gesture:`${r.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:a,gesture:`${s.name} up`})}if(e[a].keypoints){let r=Zk(e[a].keypoints);for(let s of r)t.push({hand:a,gesture:s.name})}}return t};function b0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Np(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function nw(e,t,a){let n=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/r,e.endPoint[1]/n,e.endPoint[0]/r]];return ye.cropAndResize(t,s,[0],a)}function rw(e,t){let a=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function v0(e,t=1.5){let a=Np(e),n=b0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function k0(e){let t=Np(e),a=b0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function I0e(e){return 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Object.keys(a).forEach(r=>J(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=Q(t,[-1,7,2]),n.div=xe(n.reshape,this.inputSizeTensor),n.landmarks=be(n.div,this.anchors[a]?this.anchors[a]:0);let r=te(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>J(n[s])),r}async predict(t,a){var o;let n={};n.resize=ye.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=xe(n.resize,Le.tf127),n.image=ge(n.div,Le.tf1),n.batched=this.model.execute(n.image),n.predictions=Oe(n.batched),n.slice=De(n.predictions,[0,0],[-1,1]),n.sigmoid=za(n.slice),n.scores=Oe(n.sigmoid);let r=await n.scores.data();n.boxes=De(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await ye.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=De(n.norm,[l,0],[1,-1]),u.slice=De(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=Q(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),f={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},m=rw(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(m),Object.keys(u).forEach(g=>J(u[g]))}return Object.keys(n).forEach(l=>J(n[l])),i}};var N0e=5,uw=1.65,dw=[0,5,9,13,17,1,2],E0e=0,R0e=2,pw=0,I0=class{constructor(t,a){de(this,"handDetector");de(this,"handPoseModel");de(this,"inputSize");de(this,"storedBoxes");de(this,"skipped");de(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>Kg([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return v0(k0(r),N0e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=v0(k0(a),uw);n.palmLandmarks=[];for(let r=0;r<dw.length;r++)n.palmLandmarks.push(t[dw[r]].slice(0,2));return n}transformRawCoords(t,a,n,r){let s=b0(a),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(h=>[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=Xg(n,[0,0]),u=o.map(h=>[...Kg(h,l),h[2]]),p=iw(r),c=[...Np(a),1],d=[ws(c,p[0]),ws(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>ae()-pw,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i&&(r=await this.handDetector.predict(t,a),this.skipped=0),a.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],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(a.hand.landmarks){let p=a.hand.rotation?sw(u.palmLandmarks[E0e],u.palmLandmarks[R0e]):0,c=Np(u),d=[c[0]/t.shape[2],c[1]/t.shape[1]],h=a.hand.rotation&&re.kernels.includes("rotatewithoffset")?ye.rotateWithOffset(t,p,0,d):t.clone(),f=Xg(-p,c),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=nw(m,h,[this.inputSize,this.inputSize]),y=xe(g,Le.tf255);J(g),J(h);let[x,A]=this.handPoseModel.execute(y);pw=ae(),J(y);let b=(await x.data())[0];if(J(x),b>=a.hand.minConfidence/4){let k=Q(A,[-1,3]),S=await k.array();J(A),J(k);let C=this.transformRawCoords(S,m,p,f),N=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...N,confidence:b};let $={landmarks:C,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push($)}else this.storedBoxes[l]=null;J(A)}else{let p=v0(k0(u),uw),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var cw={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]},zo,Lo,hw;async function Zg(e,t){let a=await hw.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;r<a.length;r++){let s={};if(a[r].landmarks)for(let p of Object.keys(cw))s[p]=cw[p].map(c=>a[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]<o[0]&&(o[0]=p[0]),p[1]<o[1]&&(o[1]=p[1]),p[0]>o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[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=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=A0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function fw(e){var a,n;re.initial&&(zo=null,Lo=null),!zo||!Lo?[zo,Lo]=await Promise.all([e.hand.enabled?Me((a=e.hand.detector)==null?void 0:a.modelPath):null,e.hand.landmarks?Me((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&K("cached model:",zo.modelUrl),e.debug&&K("cached model:",Lo.modelUrl));let t=zo?new w0(zo):void 0;return t&&Lo&&(hw=new I0(t,Lo)),[zo,Lo]}var Ft=[null,null],$0e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Is=[[0,0],[0,0]],_0e=["hand","fist","pinch","point","face","tip","pinchtip"],gw=4,yw=1.6,P0e=512,F0e=1.4,S0=Number.MAX_SAFE_INTEGER,Yg=0,$r=[0,0],Pt={boxes:[],hands:[]},xw={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 Aw(e){var t;if(re.initial&&(Ft[0]=null),Ft[0])e.debug&&K("cached model:",Ft[0].modelUrl);else{Kh(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Ft[0]=await Me((t=e.hand.detector)==null?void 0:t.modelPath);let a=Ft[0].executor?Object.values(Ft[0].modelSignature.inputs):void 0;Is[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Is[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ft[0]}async function bw(e){var t;if(re.initial&&(Ft[1]=null),Ft[1])e.debug&&K("cached model:",Ft[1].modelUrl);else{Ft[1]=await Me((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=Ft[1].executor?Object.values(Ft[1].modelSignature.inputs):void 0;Is[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Is[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ft[1]}async function O0e(e,t){let a=[];if(!e||!Ft[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,P0e),i=Math.round(s*r/8)*8;n.resize=ye.resizeBilinear(e,[s,i]),n.cast=Xe(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await Ft[0].executeAsync(n.cast,$0e),n.boxes=Oe(n.rawBoxes,[0,2]),n.scores=Oe(n.rawScores,[0]);let o=Ca(n.scores,1);J(o[gw]),o.splice(gw,1),n.filtered=la(o,1),J(o),n.max=ha(n.filtered,1),n.argmax=ar(n.filtered,1);let l=0;n.nms=await ye.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=De(n.boxes,d,1),f=await h.data();J(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=r0(m,F0e),y=[Math.trunc(m[0]*$r[0]),Math.trunc(m[1]*$r[1]),Math.trunc(m[2]*$r[0]),Math.trunc(m[3]*$r[1])],x=p[d],A=_0e[c[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>J(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function Jg(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ft[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=ye.cropAndResize(e,[s],[0],[Is[1][0],Is[1][1]],"bilinear"),r.div=xe(r.crop,Le.tf255),[r.score,r.keypoints]=Ft[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=Q(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/Is[1][1],c[1]/Is[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[$r[0]*(c[0]+t.boxRaw[0]),$r[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=A0(n.keypoints);for(let c of Object.keys(xw))n.annotations[c]=xw[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>J(r[l]))}return n}async function Qg(e,t){var r,s;if(!((r=Ft[0])!=null&&r.executor)||!((s=Ft[1])!=null&&s.executor)||!Ft[0].inputs[0].shape||!Ft[1].inputs[0].shape)return[];$r=[e.shape[2]||0,e.shape[1]||0],S0++;let a=(t.hand.skipTime||0)>ae()-Yg,n=S0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?Pt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>ae()-Yg,l=S0<3*(t.hand.skipFrames||0);t.skipAllowed&&Pt.hands.length===t.hand.maxDetected?Pt.hands=await Promise.all(Pt.boxes.map(p=>Jg(e,p,t))):t.skipAllowed&&o&&l&&Pt.hands.length>0?Pt.hands=await Promise.all(Pt.boxes.map(p=>Jg(e,p,t))):(Pt.boxes=await O0e(e,t),Yg=ae(),Pt.hands=await Promise.all(Pt.boxes.map(p=>Jg(e,p,t))),S0=0);let u=[...Pt.boxes];if(Pt.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p<Pt.hands.length;p++){let c=A9(Pt.hands[p].keypoints,$r);if(c.box[2]/(e.shape[2]||1)>.05&&c.box[3]/(e.shape[1]||1)>.05&&Pt.hands[p].fingerScore&&Pt.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=r0(c.box,yw),h=r0(c.boxRaw,yw);Pt.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p<Pt.hands.length;p++){let c=Er(Pt.hands[p].keypoints,$r);Pt.hands[p].box=c.box,Pt.hands[p].boxRaw=c.boxRaw}i(Pt.hands)})}var lr=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var Ep={};fr(Ep,{connected:()=>C0,horizontal:()=>e5,kpt:()=>T0,relative:()=>a5,vertical:()=>t5});var T0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],e5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],t5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],a5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],C0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var fe=lr(),n5=0;function kw(e,t){var i,o,l,u,p,c,d,h,f,m,g,y,x,A,b,k,S,C,N,$,M,R,I,_,D,W;let a=ae();if(!e)return lr();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(fe.canvas=e.canvas),e.error&&(fe.error=e.error),!fe.body||e.body.length!==fe.body.length)fe.body=JSON.parse(JSON.stringify(e.body));else for(let P=0;P<e.body.length;P++){let U=e.body[P].box.map((Z,X)=>((r-1)*fe.body[P].box[X]+Z)/r),G=e.body[P].boxRaw.map((Z,X)=>((r-1)*fe.body[P].boxRaw[X]+Z)/r),q=e.body[P].keypoints.map((Z,X)=>{var ne,ee,ce,ie,me,Se,Ne,Be,qe;return{score:Z.score,part:Z.part,position:[fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[fe.body[P].keypoints[X]?((r-1)*(((ne=fe.body[P].keypoints[X].distance)==null?void 0:ne[0])||0)+(((ee=Z.distance)==null?void 0:ee[0])||0))/r:(ce=Z.distance)==null?void 0:ce[0],fe.body[P].keypoints[X]?((r-1)*(((ie=fe.body[P].keypoints[X].distance)==null?void 0:ie[1])||0)+(((me=Z.distance)==null?void 0:me[1])||0))/r:(Se=Z.distance)==null?void 0:Se[1],fe.body[P].keypoints[X]?((r-1)*(((Ne=fe.body[P].keypoints[X].distance)==null?void 0:Ne[2])||0)+(((Be=Z.distance)==null?void 0:Be[2])||0))/r:(qe=Z.distance)==null?void 0:qe[2]]}}),H={},B={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?B=o0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?B=a0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(B=Ep);for(let[Z,X]of Object.entries(B.connected)){let ne=[];for(let ee=0;ee<X.length-1;ee++){let ce=q.find(me=>me.part===X[ee]),ie=q.find(me=>me.part===X[ee+1]);ce&&ie&&ne.push([ce.position,ie.position])}H[Z]=ne}fe.body[P]={...e.body[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!fe.hand||e.hand.length!==fe.hand.length)fe.hand=JSON.parse(JSON.stringify(e.hand));else for(let P=0;P<e.hand.length;P++){let U=e.hand[P].box.map((B,Z)=>((r-1)*fe.hand[P].box[Z]+B)/r),G=e.hand[P].boxRaw.map((B,Z)=>((r-1)*fe.hand[P].boxRaw[Z]+B)/r);fe.hand[P].keypoints.length!==e.hand[P].keypoints.length&&(fe.hand[P].keypoints=e.hand[P].keypoints);let q=e.hand[P].keypoints&&e.hand[P].keypoints.length>0?e.hand[P].keypoints.map((B,Z)=>B.map((X,ne)=>((r-1)*(fe.hand[P].keypoints[Z][ne]||1)+(X||0))/r)):[],H={};if(Object.keys(fe.hand[P].annotations).length!==Object.keys(e.hand[P].annotations).length)fe.hand[P].annotations=e.hand[P].annotations,H=fe.hand[P].annotations;else if(e.hand[P].annotations)for(let B of Object.keys(e.hand[P].annotations))H[B]=(c=(p=(u=e.hand[P])==null?void 0:u.annotations)==null?void 0:p[B])!=null&&c[0]?e.hand[P].annotations[B].map((Z,X)=>Z.map((ne,ee)=>((r-1)*fe.hand[P].annotations[B][X][ee]+ne)/r)):null;fe.hand[P]={...e.hand[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!fe.face||e.face.length!==fe.face.length)fe.face=JSON.parse(JSON.stringify(e.face));else for(let P=0;P<e.face.length;P++){let U=e.face[P].box.map((H,B)=>((r-1)*fe.face[P].box[B]+H)/r),G=e.face[P].boxRaw.map((H,B)=>((r-1)*fe.face[P].boxRaw[B]+H)/r),q=e.face[P].annotations;if(Object.keys(fe.face[P].annotations).length!==Object.keys(e.face[P].annotations).length)fe.face[P].annotations=e.face[P].annotations,q=fe.face[P].annotations;else if(e.face[P].annotations)for(let H of Object.keys(e.face[P].annotations))q[H]=(f=(h=(d=e.face[P])==null?void 0:d.annotations)==null?void 0:h[H])!=null&&f[0]?e.face[P].annotations[H].map((B,Z)=>B.map((X,ne)=>((r-1)*fe.face[P].annotations[H][Z][ne]+X)/r)):null;if(e.face[P].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(m=e.face[P].rotation)==null?void 0:m.matrix,H.angle={roll:((r-1)*(((y=(g=fe.face[P].rotation)==null?void 0:g.angle)==null?void 0:y.roll)||0)+(((A=(x=e.face[P].rotation)==null?void 0:x.angle)==null?void 0:A.roll)||0))/r,yaw:((r-1)*(((k=(b=fe.face[P].rotation)==null?void 0:b.angle)==null?void 0:k.yaw)||0)+(((C=(S=e.face[P].rotation)==null?void 0:S.angle)==null?void 0:C.yaw)||0))/r,pitch:((r-1)*((($=(N=fe.face[P].rotation)==null?void 0:N.angle)==null?void 0:$.pitch)||0)+(((R=(M=e.face[P].rotation)==null?void 0:M.angle)==null?void 0:R.pitch)||0))/r},H.gaze={bearing:((r-1)*(((I=fe.face[P].rotation)==null?void 0:I.gaze.bearing)||0)+(((_=e.face[P].rotation)==null?void 0:_.gaze.bearing)||0))/r,strength:((r-1)*(((D=fe.face[P].rotation)==null?void 0:D.gaze.strength)||0)+(((W=e.face[P].rotation)==null?void 0:W.gaze.strength)||0))/r},fe.face[P]={...e.face[P],rotation:H,box:U,boxRaw:G,annotations:q}}else fe.face[P]={...e.face[P],box:U,boxRaw:G,annotations:q}}if(!fe.object||e.object.length!==fe.object.length)fe.object=JSON.parse(JSON.stringify(e.object));else for(let P=0;P<e.object.length;P++){let 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a={};a.resize=ye.resizeBilinear(e,[xa.inputs[0].shape?xa.inputs[0].shape[1]:0,xa.inputs[0].shape?xa.inputs[0].shape[2]:0],!1),a.norm=xe(a.resize,Le.tf255),a.res=xa.execute(a.norm),a.squeeze=Oe(a.res,[0]),a.alpha=ye.resizeBilinear(a.squeeze,[e.shape[1]||0,e.shape[2]||0]),a.mul=te(a.alpha,Le.tf255);let n;switch(t.segmentation.mode||"default"){case"default":a.input=Oe(e),a.concat=st([a.input,a.mul],-1),n=Xe(a.concat,"int32");break;case"alpha":n=Xe(a.mul,"int32");break;default:n=Ue(0)}return Object.keys(a).forEach(s=>J(a[s])),n}function F0(e,t,a){var u,p;if(!t||!((u=e==null?void 0:e.config)!=null&&u.validateModels))return null;let n=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"],r=["biasadd","fusedbatchnormv3","matmul","switch","shape","merge","split","broadcastto"],s=[],i=[],o=t.modelUrl,l=t.executor;if((p=l==null?void 0:l.graph)!=null&&p.nodes)for(let c of Object.values(l.graph.nodes)){let 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a={};a.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?V9(this.instance.config):null,a.antispoof=this.instance.config.face.enabled&&((n=this.instance.config.face.antispoof)!=null&&n.enabled)&&!this.models.antispoof?hk(this.instance.config):null,a.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?yk(this.instance.config):null,a.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?lk(this.instance.config):null,a.emotion=this.instance.config.face.enabled&&((i=this.instance.config.face.emotion)!=null&&i.enabled)&&!this.models.emotion?rk(this.instance.config):null,a.iris=this.instance.config.face.enabled&&((o=this.instance.config.face.iris)!=null&&o.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?X9(this.instance.config):null,a.facemesh=this.instance.config.face.enabled&&((u=this.instance.config.face.mesh)!=null&&u.enabled)&&!this.models.facemesh?Q9(this.instance.config):null,a.gear=this.instance.config.face.enabled&&((p=this.instance.config.face.gear)!=null&&p.enabled)&&!this.models.gear?vk(this.instance.config):null,a.ssrnetage=this.instance.config.face.enabled&&((c=this.instance.config.face.ssrnet)!=null&&c.enabled)&&!this.models.ssrnetage?Sk(this.instance.config):null,a.ssrnetgender=this.instance.config.face.enabled&&((d=this.instance.config.face.ssrnet)!=null&&d.enabled)&&!this.models.ssrnetgender?Ek(this.instance.config):null,a.mobilefacenet=this.instance.config.face.enabled&&((h=this.instance.config.face.mobilefacenet)!=null&&h.enabled)&&!this.models.mobilefacenet?Pk(this.instance.config):null,a.insightface=this.instance.config.face.enabled&&((f=this.instance.config.face.insightface)!=null&&f.enabled)&&!this.models.insightface?Lk(this.instance.config):null,a.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((m=this.instance.config.body.modelPath)!=null&&m.includes("blazepose"))?I9(this.instance.config):null,a.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?w9(this.instance.config):null,a.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((g=this.instance.config.body.modelPath)!=null&&g.includes("efficientpose"))?R9(this.instance.config):null,a.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((y=this.instance.config.body.modelPath)!=null&&y.includes("movenet"))?Rw(this.instance.config):null,a.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((x=this.instance.config.body.modelPath)!=null&&x.includes("posenet"))?Ww(this.instance.config):null,a.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((b=(A=this.instance.config.hand.detector)==null?void 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bn,$o=224,m9,Vhe=5,a0=[8,16,32,32,32];function Uhe(){let e=[],t=0;for(;t<Vhe;){let a=0,n=t;for(;n<a0.length&&a0[n]===a0[t];)a+=2,n++;let r=a0[t],s=Math.ceil($o/r),i=Math.ceil($o/r);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<a;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}m9={x:Ht(e.map(a=>a.x)),y:Ht(e.map(a=>a.y))}}async function g9(e){if(re.initial&&(bn=null),!bn&&e.body.detector&&e.body.detector.modelPath){bn=await Me(e.body.detector.modelPath);let t=bn!=null&&bn.executor?Object.values(bn.modelSignature.inputs):void 0;$o=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&bn&&K("cached model:",bn.modelUrl);return Uhe(),bn}var f9=[5,5];function Ghe(e,t){return Oe(()=>{let a=Ia(e,12,1),n=De(a[0]),r=De(a[1]),s=De(a[2]),i=De(a[3]);n=be(xe(n,$o),t.x),r=be(xe(r,$o),t.y),s=te(xe(s,$o),f9[0]),i=te(xe(i,$o),f9[1]);let o=ge(n,xe(s,2)),l=ge(r,xe(i,2)),u=be(o,s),p=be(l,i);return la([o,l,u,p],1)})}async function Hhe(e,t,a,n){var u,p;let r=[],s={};s.boxes=Ghe(e,m9),s.scores=za(t),s.nms=await ye.nonMaxSuppressionAsync(s.boxes,s.scores,1,((u=a.body.detector)==null?void 0:u.minConfidence)||.1,((p=a.body.detector)==null?void 0:p.iouThreshold)||.1);let i=await s.nms.data(),o=await s.scores.data(),l=await s.boxes.array();for(let c of Array.from(i)){let d=o[c],h=l[c],f=[Math.round(h[0]*n[0]),Math.round(h[1]*n[1]),Math.round(h[2]*n[0]),Math.round(h[3]*n[1])],m={score:d,boxRaw:h,box:f};r.push(m)}return Object.keys(s).forEach(c=>J(s[c])),r}async function y9(e,t,a){let n={};n.res=bn==null?void 0:bn.execute(e,["Identity"]),n.logitsRaw=Fe(n.res,[0,0,0],[1,-1,1]),n.boxesRaw=Fe(n.res,[0,0,1],[1,-1,-1]),n.logits=De(n.logitsRaw),n.boxes=De(n.boxesRaw);let r=await Hhe(n.boxes,n.logits,t,a);return Object.keys(n).forEach(s=>J(n[s])),r}function Er(e,t=[1,1]){let 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r=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(f=>J(n[f]));let o=[],l=5;for(let f=0;f<s.length/l;f++){let m=b9(s[l*f+3]),g=b9(s[l*f+4]),y=Math.trunc(100*m*g*r)/100,x=[s[l*f+0]/ig,s[l*f+1]/ig,s[l*f+2]+0],A=[Math.trunc(a[0]*x[0]),Math.trunc(a[1]*x[1]),x[2]],b=[i[l*f+0],i[l*f+1],i[l*f+2]+0];o.push({part:ng[f],positionRaw:x,position:A,distance:b,score:y})}if(r<(t.body.minConfidence||0))return null;Xhe(o);let u=qhe(o,a),p=u.map(f=>f.position),c=Er(p,[a[0],a[1]]),d={};for(let[f,m]of Object.entries(rg)){let g=[];for(let y=0;y<m.length-1;y++){let x=u.find(b=>b.part===m[y]),A=u.find(b=>b.part===m[y+1]);x&&A&&g.push([x.position,A.position])}d[f]=g}return{id:0,score:Math.trunc(100*r)/100,box:c.box,boxRaw:c.boxRaw,keypoints:u,annotations:d}}async function og(e,t){var s,i,o;let a=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>ae()-A9,r=sg<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&s0!==null)sg++;else{let l=[];if((i=(s=t.body)==null?void 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t={};t.boxStarts=Fe(e,[0,1],[-1,2]),t.centers=be(t.boxStarts,c0),t.boxSizes=Fe(e,[0,3],[-1,2]),t.boxSizesNormalized=xe(t.boxSizes,wu),t.centersNormalized=xe(t.centers,wu),t.halfBoxSize=xe(t.boxSizesNormalized,Le.tf2),t.starts=ge(t.centersNormalized,t.halfBoxSize),t.ends=be(t.centersNormalized,t.halfBoxSize),t.startNormalized=te(t.starts,wu),t.endNormalized=te(t.ends,wu);let a=ru([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>J(t[n])),a}async function U9(e,t){var o,l,u,p,c,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let a={};a.resized=ye.resizeBilinear(e,[ms,ms]),a.div=xe(a.resized,Le.tf127),a.normalized=ge(a.div,Le.tf05);let n=Ln==null?void 0:Ln.execute(a.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((f,m)=>f.size-m.size);a.concat384=st([h[0],h[2]],2),a.concat512=st([h[1],h[3]],2),a.concat=st([a.concat512,a.concat384],1),a.batch=De(a.concat,[0])}else 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A=or.boxes[x],b=0,k,S={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([b,k,S.tensor]=z9((p=t.face.detector)==null?void 0:p.rotation,A,e,(c=t.face.mesh)!=null&&c.enabled?Tp:W9()),t.filter.equalization){let C=S.tensor?await Vh(S.tensor):void 0;J(S.tensor),C&&(S.tensor=C)}if(S.boxScore=Math.round(100*A.confidence)/100,!((d=t.face.mesh)!=null&&d.enabled)||!(wt!=null&&wt.executor)){S.box=l0(A,e),S.boxRaw=u0(A,e),S.score=S.boxScore,S.size=A.size,S.mesh=A.landmarks,S.meshRaw=S.mesh.map(C=>[C[0]/(e.shape[2]||0),C[1]/(e.shape[1]||0),(C[2]||0)/o]);for(let C of Object.keys(No))S.annotations[C]=[S.mesh[No[C]]]}else if(!wt)t.debug&&K("face mesh detection requested, but model is not loaded");else{if((h=t.face.attention)!=null&&h.enabled&&!re.kernels.includes("atan2"))return t.face.attention.enabled=!1,J(S.tensor),r;let C=wt.execute(S.tensor),$=await 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ee={...h[B],id:B};c.age&&(ee.age=c.age),c.gender&&(ee.gender=c.gender),c.genderScore&&(ee.genderScore=c.genderScore),c.descriptor&&(ee.embedding=c.descriptor),c.race&&(ee.race=c.race),i&&(ee.emotion=i),u&&(ee.real=u),p&&(ee.live=p),X>0&&(ee.distance=X),Z&&(ee.rotation=Z),ne&&(ee.tensor=ne),d.push(ee),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),d};var 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t},Jk=e=>{if(!e)return[];let t=[];for(let a=0;a<e.length;a++)if(e[a].mesh&&e[a].mesh.length>450){let n=(e[a].mesh[33][2]||0)-(e[a].mesh[263][2]||0),r=e[a].mesh[33][0]-e[a].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:a,gesture:"facing center"}):t.push({face:a,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[a].mesh[374][1]-e[a].mesh[386][1])/Math.abs(e[a].mesh[443][1]-e[a].mesh[450][1])<.2&&t.push({face:a,gesture:"blink left eye"}),Math.abs(e[a].mesh[145][1]-e[a].mesh[159][1])/Math.abs(e[a].mesh[223][1]-e[a].mesh[230][1])<.2&&t.push({face:a,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[a].mesh[13][1]-e[a].mesh[14][1])/Math.abs(e[a].mesh[10][1]-e[a].mesh[152][1]));o>10&&t.push({face:a,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[a].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:a,gesture:`head ${l<0?"up":"down"}`})}return t},Qk=e=>{var a,n,r,s;if(!e)return[];let t=[];for(let i=0;i<e.length;i++){if(!((n=(a=e[i].annotations)==null?void 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b0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Cp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function nw(e,t,a){let n=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/r,e.endPoint[1]/n,e.endPoint[0]/r]];return ye.cropAndResize(t,s,[0],a)}function rw(e,t){let a=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function v0(e,t=1.5){let a=Cp(e),n=b0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function k0(e){let t=Cp(e),a=b0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function I0e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function sw(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return I0e(a)}var tw=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ws(e,t){let a=0;for(let n=0;n<e.length;n++)a+=e[n]*t[n];return a}function S0e(e,t){let a=[];for(let n=0;n<e.length;n++)a.push(e[n][t]);return a}function aw(e,t){let a=[],n=e.length;for(let r=0;r<n;r++){a.push([]);for(let s=0;s<n;s++)a[r].push(ws(e[r],S0e(t,s)))}return a}function qg(e,t){let a=Math.cos(e),n=Math.sin(e),r=[[a,-n,0],[n,a,0],[0,0,1]],s=tw(t[0],t[1]),i=aw(s,r),o=tw(-t[0],-t[1]);return aw(i,o)}function iw(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],a=[e[0][2],e[1][2]],n=[-ws(t[0],a),-ws(t[1],a)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]}function Xg(e,t){return[ws(e,t[0]),ws(e,t[1])]}var 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Object.keys(a).forEach(r=>J(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=Q(t,[-1,7,2]),n.div=xe(n.reshape,this.inputSizeTensor),n.landmarks=be(n.div,this.anchors[a]?this.anchors[a]:0);let r=te(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>J(n[s])),r}async predict(t,a){var o;let n={};n.resize=ye.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=xe(n.resize,Le.tf127),n.image=ge(n.div,Le.tf1),n.batched=this.model.execute(n.image),n.predictions=De(n.batched),n.slice=Fe(n.predictions,[0,0],[-1,1]),n.sigmoid=za(n.slice),n.scores=De(n.sigmoid);let r=await n.scores.data();n.boxes=Fe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await ye.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=Fe(n.norm,[l,0],[1,-1]),u.slice=Fe(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=Q(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),f={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},m=rw(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(m),Object.keys(u).forEach(g=>J(u[g]))}return Object.keys(n).forEach(l=>J(n[l])),i}};var N0e=5,uw=1.65,dw=[0,5,9,13,17,1,2],E0e=0,R0e=2,pw=0,I0=class{constructor(t,a){de(this,"handDetector");de(this,"handPoseModel");de(this,"inputSize");de(this,"storedBoxes");de(this,"skipped");de(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>Xg([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return v0(k0(r),N0e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=v0(k0(a),uw);n.palmLandmarks=[];for(let r=0;r<dw.length;r++)n.palmLandmarks.push(t[dw[r]].slice(0,2));return n}transformRawCoords(t,a,n,r){let s=b0(a),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(h=>[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=qg(n,[0,0]),u=o.map(h=>[...Xg(h,l),h[2]]),p=iw(r),c=[...Cp(a),1],d=[ws(c,p[0]),ws(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>ae()-pw,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i&&(r=await this.handDetector.predict(t,a),this.skipped=0),a.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],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(a.hand.landmarks){let p=a.hand.rotation?sw(u.palmLandmarks[E0e],u.palmLandmarks[R0e]):0,c=Cp(u),d=[c[0]/t.shape[2],c[1]/t.shape[1]],h=a.hand.rotation&&re.kernels.includes("rotatewithoffset")?ye.rotateWithOffset(t,p,0,d):t.clone(),f=qg(-p,c),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=nw(m,h,[this.inputSize,this.inputSize]),y=xe(g,Le.tf255);J(g),J(h);let[x,A]=this.handPoseModel.execute(y);pw=ae(),J(y);let b=(await x.data())[0];if(J(x),b>=a.hand.minConfidence/4){let k=Q(A,[-1,3]),S=await k.array();J(A),J(k);let C=this.transformRawCoords(S,m,p,f),N=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...N,confidence:b};let $={landmarks:C,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push($)}else this.storedBoxes[l]=null;J(A)}else{let p=v0(k0(u),uw),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var cw={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]},zo,Lo,hw;async function Kg(e,t){let a=await hw.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;r<a.length;r++){let s={};if(a[r].landmarks)for(let p of Object.keys(cw))s[p]=cw[p].map(c=>a[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]<o[0]&&(o[0]=p[0]),p[1]<o[1]&&(o[1]=p[1]),p[0]>o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[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=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=A0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function fw(e){var a,n;re.initial&&(zo=null,Lo=null),!zo||!Lo?[zo,Lo]=await Promise.all([e.hand.enabled?Me((a=e.hand.detector)==null?void 0:a.modelPath):null,e.hand.landmarks?Me((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&K("cached model:",zo.modelUrl),e.debug&&K("cached model:",Lo.modelUrl));let t=zo?new w0(zo):void 0;return t&&Lo&&(hw=new I0(t,Lo)),[zo,Lo]}var Ft=[null,null],$0e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Is=[[0,0],[0,0]],_0e=["hand","fist","pinch","point","face","tip","pinchtip"],gw=4,yw=1.6,P0e=512,F0e=1.4,S0=Number.MAX_SAFE_INTEGER,Zg=0,$r=[0,0],Pt={boxes:[],hands:[]},xw={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 Aw(e){var t;if(re.initial&&(Ft[0]=null),Ft[0])e.debug&&K("cached model:",Ft[0].modelUrl);else{Xh(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Ft[0]=await Me((t=e.hand.detector)==null?void 0:t.modelPath);let a=Ft[0].executor?Object.values(Ft[0].modelSignature.inputs):void 0;Is[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Is[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ft[0]}async function bw(e){var t;if(re.initial&&(Ft[1]=null),Ft[1])e.debug&&K("cached model:",Ft[1].modelUrl);else{Ft[1]=await Me((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=Ft[1].executor?Object.values(Ft[1].modelSignature.inputs):void 0;Is[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Is[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ft[1]}async function O0e(e,t){let a=[];if(!e||!Ft[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,P0e),i=Math.round(s*r/8)*8;n.resize=ye.resizeBilinear(e,[s,i]),n.cast=Xe(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await Ft[0].executeAsync(n.cast,$0e),n.boxes=De(n.rawBoxes,[0,2]),n.scores=De(n.rawScores,[0]);let o=Ca(n.scores,1);J(o[gw]),o.splice(gw,1),n.filtered=la(o,1),J(o),n.max=ha(n.filtered,1),n.argmax=ar(n.filtered,1);let l=0;n.nms=await ye.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=Fe(n.boxes,d,1),f=await h.data();J(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=n0(m,F0e),y=[Math.trunc(m[0]*$r[0]),Math.trunc(m[1]*$r[1]),Math.trunc(m[2]*$r[0]),Math.trunc(m[3]*$r[1])],x=p[d],A=_0e[c[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>J(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function Yg(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ft[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=ye.cropAndResize(e,[s],[0],[Is[1][0],Is[1][1]],"bilinear"),r.div=xe(r.crop,Le.tf255),[r.score,r.keypoints]=Ft[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=Q(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/Is[1][1],c[1]/Is[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[$r[0]*(c[0]+t.boxRaw[0]),$r[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=A0(n.keypoints);for(let c of Object.keys(xw))n.annotations[c]=xw[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>J(r[l]))}return n}async function Jg(e,t){var r,s;if(!((r=Ft[0])!=null&&r.executor)||!((s=Ft[1])!=null&&s.executor)||!Ft[0].inputs[0].shape||!Ft[1].inputs[0].shape)return[];$r=[e.shape[2]||0,e.shape[1]||0],S0++;let a=(t.hand.skipTime||0)>ae()-Zg,n=S0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?Pt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>ae()-Zg,l=S0<3*(t.hand.skipFrames||0);t.skipAllowed&&Pt.hands.length===t.hand.maxDetected?Pt.hands=await Promise.all(Pt.boxes.map(p=>Yg(e,p,t))):t.skipAllowed&&o&&l&&Pt.hands.length>0?Pt.hands=await Promise.all(Pt.boxes.map(p=>Yg(e,p,t))):(Pt.boxes=await O0e(e,t),Zg=ae(),Pt.hands=await Promise.all(Pt.boxes.map(p=>Yg(e,p,t))),S0=0);let u=[...Pt.boxes];if(Pt.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p<Pt.hands.length;p++){let c=x9(Pt.hands[p].keypoints,$r);if(c.box[2]/(e.shape[2]||1)>.05&&c.box[3]/(e.shape[1]||1)>.05&&Pt.hands[p].fingerScore&&Pt.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=n0(c.box,yw),h=n0(c.boxRaw,yw);Pt.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p<Pt.hands.length;p++){let c=Er(Pt.hands[p].keypoints,$r);Pt.hands[p].box=c.box,Pt.hands[p].boxRaw=c.boxRaw}i(Pt.hands)})}var lr=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var Np={};fr(Np,{connected:()=>C0,horizontal:()=>Qg,kpt:()=>T0,relative:()=>t5,vertical:()=>e5});var T0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Qg=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],e5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],t5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],C0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var fe=lr(),a5=0;function kw(e,t){var i,o,l,u,p,c,d,h,f,m,g,y,x,A,b,k,S,C,N,$,M,R,I,_,D,W;let a=ae();if(!e)return lr();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(fe.canvas=e.canvas),e.error&&(fe.error=e.error),!fe.body||e.body.length!==fe.body.length)fe.body=JSON.parse(JSON.stringify(e.body));else for(let P=0;P<e.body.length;P++){let U=e.body[P].box.map((Z,X)=>((r-1)*fe.body[P].box[X]+Z)/r),G=e.body[P].boxRaw.map((Z,X)=>((r-1)*fe.body[P].boxRaw[X]+Z)/r),q=e.body[P].keypoints.map((Z,X)=>{var ne,ee,ce,ie,me,Se,Ne,Be,qe;return{score:Z.score,part:Z.part,position:[fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],fe.body[P].keypoints[X]?((r-1)*(fe.body[P].keypoints[X].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[fe.body[P].keypoints[X]?((r-1)*(((ne=fe.body[P].keypoints[X].distance)==null?void 0:ne[0])||0)+(((ee=Z.distance)==null?void 0:ee[0])||0))/r:(ce=Z.distance)==null?void 0:ce[0],fe.body[P].keypoints[X]?((r-1)*(((ie=fe.body[P].keypoints[X].distance)==null?void 0:ie[1])||0)+(((me=Z.distance)==null?void 0:me[1])||0))/r:(Se=Z.distance)==null?void 0:Se[1],fe.body[P].keypoints[X]?((r-1)*(((Ne=fe.body[P].keypoints[X].distance)==null?void 0:Ne[2])||0)+(((Be=Z.distance)==null?void 0:Be[2])||0))/r:(qe=Z.distance)==null?void 0:qe[2]]}}),H={},B={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?B=i0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?B=t0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(B=Np);for(let[Z,X]of Object.entries(B.connected)){let ne=[];for(let ee=0;ee<X.length-1;ee++){let ce=q.find(me=>me.part===X[ee]),ie=q.find(me=>me.part===X[ee+1]);ce&&ie&&ne.push([ce.position,ie.position])}H[Z]=ne}fe.body[P]={...e.body[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!fe.hand||e.hand.length!==fe.hand.length)fe.hand=JSON.parse(JSON.stringify(e.hand));else for(let P=0;P<e.hand.length;P++){let U=e.hand[P].box.map((B,Z)=>((r-1)*fe.hand[P].box[Z]+B)/r),G=e.hand[P].boxRaw.map((B,Z)=>((r-1)*fe.hand[P].boxRaw[Z]+B)/r);fe.hand[P].keypoints.length!==e.hand[P].keypoints.length&&(fe.hand[P].keypoints=e.hand[P].keypoints);let q=e.hand[P].keypoints&&e.hand[P].keypoints.length>0?e.hand[P].keypoints.map((B,Z)=>B.map((X,ne)=>((r-1)*(fe.hand[P].keypoints[Z][ne]||1)+(X||0))/r)):[],H={};if(Object.keys(fe.hand[P].annotations).length!==Object.keys(e.hand[P].annotations).length)fe.hand[P].annotations=e.hand[P].annotations,H=fe.hand[P].annotations;else if(e.hand[P].annotations)for(let B of Object.keys(e.hand[P].annotations))H[B]=(c=(p=(u=e.hand[P])==null?void 0:u.annotations)==null?void 0:p[B])!=null&&c[0]?e.hand[P].annotations[B].map((Z,X)=>Z.map((ne,ee)=>((r-1)*fe.hand[P].annotations[B][X][ee]+ne)/r)):null;fe.hand[P]={...e.hand[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!fe.face||e.face.length!==fe.face.length)fe.face=JSON.parse(JSON.stringify(e.face));else for(let P=0;P<e.face.length;P++){let U=e.face[P].box.map((H,B)=>((r-1)*fe.face[P].box[B]+H)/r),G=e.face[P].boxRaw.map((H,B)=>((r-1)*fe.face[P].boxRaw[B]+H)/r),q=e.face[P].annotations;if(Object.keys(fe.face[P].annotations).length!==Object.keys(e.face[P].annotations).length)fe.face[P].annotations=e.face[P].annotations,q=fe.face[P].annotations;else if(e.face[P].annotations)for(let H of Object.keys(e.face[P].annotations))q[H]=(f=(h=(d=e.face[P])==null?void 0:d.annotations)==null?void 0:h[H])!=null&&f[0]?e.face[P].annotations[H].map((B,Z)=>B.map((X,ne)=>((r-1)*fe.face[P].annotations[H][Z][ne]+X)/r)):null;if(e.face[P].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(m=e.face[P].rotation)==null?void 0:m.matrix,H.angle={roll:((r-1)*(((y=(g=fe.face[P].rotation)==null?void 0:g.angle)==null?void 0:y.roll)||0)+(((A=(x=e.face[P].rotation)==null?void 0:x.angle)==null?void 0:A.roll)||0))/r,yaw:((r-1)*(((k=(b=fe.face[P].rotation)==null?void 0:b.angle)==null?void 0:k.yaw)||0)+(((C=(S=e.face[P].rotation)==null?void 0:S.angle)==null?void 0:C.yaw)||0))/r,pitch:((r-1)*((($=(N=fe.face[P].rotation)==null?void 0:N.angle)==null?void 0:$.pitch)||0)+(((R=(M=e.face[P].rotation)==null?void 0:M.angle)==null?void 0:R.pitch)||0))/r},H.gaze={bearing:((r-1)*(((I=fe.face[P].rotation)==null?void 0:I.gaze.bearing)||0)+(((_=e.face[P].rotation)==null?void 0:_.gaze.bearing)||0))/r,strength:((r-1)*(((D=fe.face[P].rotation)==null?void 0:D.gaze.strength)||0)+(((W=e.face[P].rotation)==null?void 0:W.gaze.strength)||0))/r},fe.face[P]={...e.face[P],rotation:H,box:U,boxRaw:G,annotations:q}}else fe.face[P]={...e.face[P],box:U,boxRaw:G,annotations:q}}if(!fe.object||e.object.length!==fe.object.length)fe.object=JSON.parse(JSON.stringify(e.object));else for(let P=0;P<e.object.length;P++){let 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g=d.execute(m);r.push(c),Array.isArray(g)?g.forEach(y=>J(y)):J(g)}catch(g){e.config.debug&&K("compile fail model:",c)}J(m)}let s=await a.checkCompileCompletionAsync();a.getUniformLocations(),e.config.debug&&K("compile pass:",{models:r,kernels:s.length}),V().set("ENGINE_COMPILE_ONLY",!1);let i=vt().state.numTensors;i-n>0&&K("tensor leak:",i-n)}async function Zw(e,t){await Ip(e,!1);let a=ae();return e.state="warmup",t&&(e.config=Nt(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?lr():new Promise(async n=>{await e.models.load(),await lfe(e);let r=await ofe(e),s=ae();e.config.debug&&K("warmup",e.config.warmup,Math.round(s-a),"ms"),e.emit("warmup"),n(r)})}var $u,_p,Pp,z0,Ss,v5=class{constructor(t){de(this,"version");de(this,"config");de(this,"result");de(this,"state");de(this,"process");de(this,"tf");de(this,"env",re);de(this,"draw",t0);de(this,"match",N0);de(this,"models");de(this,"events");de(this,"faceTriangulation");de(this,"faceUVMap");de(this,"performance");Gn(this,$u,void 0);Gn(this,_p,void 0);Gn(this,Pp,void 0);de(this,"analyze",(...t)=>{if(!Ha(this,_p))return;let a=this.tf.engine().state.numTensors,n=Ha(this,$u);mr(this,$u,a);let r=a-n;r!==0&&K(...t,r)});Gn(this,z0,t=>{if(!Ha(this,Pp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof pt))return"input must be a tensor";try{this.tf.getBackend()}catch(a){return"backend not loaded"}return null});de(this,"webcam",new Xh);de(this,"emit",t=>{var a;(a=this.events)!=null&&a.dispatchEvent&&this.events.dispatchEvent(new Event(t))});Gn(this,Ss,{});let a=(Ap.tfjs||t3).replace(/-(.*)/,"");So.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${a}/dist/`,So.modelBasePath=re.browser?"../models/":"file://models/",this.version=K3,Object.defineProperty(this,"version",{value:K3}),this.config=JSON.parse(JSON.stringify(So)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Nt(this.config,t)),l9(this.config),this.tf=je,this.state="idle",mr(this,$u,0),mr(this,_p,!1),mr(this,Pp,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new $p(this),ag(),this.result=lr(),this.process={tensor:null,canvas:null},this.faceTriangulation=ek,this.faceUVMap=tk,F0(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&K(`version: ${this.version}`),this.config.debug&&K(`tfjs version: ${this.tf.version["tfjs-core"]}`);let n=JSON.parse(JSON.stringify(this.env));delete n.kernels,delete n.initial,delete n.perfadd,this.config.debug&&K("environment:",n)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(So)),this.config.backend=t,q3(),re.initial=!0}validate(t){let a=G3(So,t||this.config);return a.length===0&&(this.config=Nt(this.config,t)),a}now(){return ae()}image(t,a=!1){return jh(t,this.config,a)}async segmentation(t,a){var s,i,o;if(a&&(this.config=Nt(this.config,a)),!this.config.segmentation.enabled)return null;let n=await jh(t,this.config);if(!n.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Hw(n.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("meet")&&(r=await ww(n.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("selfie")&&(r=await qw(n.tensor,this.config)),J(n.tensor),r}compare(t,a){return o9(this.config,t,a)}async init(){await Ip(this,!0),await this.tf.ready(),q3()}async load(t){this.state="load";let a=ae(),n=Object.values(this.models.models).filter(i=>i).length;t&&(this.config=Nt(this.config,t)),this.env.initial&&(await Ip(this,!1)||K("error: backend check failed"),await Qd(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&K("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(ae()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return kw(t,this.config)}async warmup(t){let a=ae(),n=await Zw(this,t),r=ae();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let l=Number(o.kernelTimeMs)||0;r[o.name]?r[o.name]+=l:r[o.name]=l,s+=l}let i=[];Object.entries(r).forEach(o=>i.push({kernel:o[0],time:o[1],perc:0}));for(let o of i)o.perc=Math.round(1e3*o.time/s)/1e3,o.time=Math.round(1e3*o.time)/1e3;return i.sort((o,l)=>l.time-o.time),i.length=20,i}async detect(t,a){return this.state="detect",new Promise(async n=>{var g,y,x,A,b,k,S,C,N,$,M,R,I,_,D,W,P,U,G,q,H;this.state="config";let r;this.config=Nt(this.config,a),this.state="check";let s=Ha(this,z0).call(this,t);s&&(K(s,t),this.emit("error"),n(lr(s)));let i=ae();await this.load(),r=ae(),this.state="image";let o=await jh(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Get Image:"),!o.tensor){this.config.debug&&K("could not convert input to tensor"),this.emit("error"),n(lr("could not convert input to tensor"));return}this.emit("image"),r=ae(),this.config.skipAllowed=await i9(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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(ae()-r):Math.trunc(ae()-r),this.analyze("Check Changed:");let l=[],u=[],p=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Hg(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=ae(),l=this.config.face.enabled?await Hg(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),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 d=this.config.body.maxDetected===-1?Nt(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?m5(o.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?og(o.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?fg(o.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?l5(o.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ae(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await m5(o.tensor,d):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?u=this.config.body.enabled?await og(o.tensor,d):[]:(S=this.config.body.modelPath)!=null&&S.includes("efficientpose")?u=this.config.body.enabled?await fg(o.tensor,d):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(u=this.config.body.enabled?await l5(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Nt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(($=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&$.includes("handdetect")?p=this.config.hand.enabled?Zg(o.tensor,h):[]:(R=(M=this.config.hand.detector)==null?void 0:M.modelPath)!=null&&R.includes("handtrack")&&(p=this.config.hand.enabled?Qg(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ae(),(_=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?await Zg(o.tensor,h):[]:(W=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&W.includes("handtrack")&&(p=this.config.hand.enabled?await Qg(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((P=this.config.object.modelPath)!=null&&P.includes("nanodet")?c=this.config.object.enabled?d5(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?dg(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ae(),(G=this.config.object.modelPath)!=null&&G.includes("nanodet")?c=this.config.object.enabled?await d5(o.tensor,this.config):[]:(q=this.config.object.modelPath)!=null&&q.includes("centernet")&&(c=this.config.object.enabled?await dg(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ae(),f=[...Jk(l),...Yk(u),...ew(p),...Qk(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ae()-i):Math.trunc(ae()-i);let m=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:f,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:m[2],height:m[1],get persons(){return Kw(l,u,p,f,m)}},J(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(Ha(this,Ss)[t.id]||(this.config.debug&&K("video start",t.id),Ha(this,Ss)[t.id]=!0),!t.paused&&Ha(this,Ss)[t.id]&&t.readyState>=2&&await this.detect(t),n>0&&await this.sleep(n),Ha(this,Ss)[t.id]&&requestAnimationFrame(()=>this.video(t,a,n))):(this.config.debug&&K("video stop",t.id),Ha(this,Ss)[t.id]=!1)}};$u=new WeakMap,_p=new WeakMap,Pp=new WeakMap,z0=new WeakMap,Ss=new WeakMap;return FI(dfe);})();
+2Q==`;async function rfe(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),a,n;switch(e.config.warmup){case"face":a=await t(O0);break;case"body":case"full":a=await t(D0);break;default:a=null}if(a){let r=await createImageBitmap(a);n=await e.detect(r,e.config),r.close()}return n}async function sfe(e){return new Promise(t=>{let a;switch(e.config.warmup){case"face":a="data:image/jpeg;base64,"+O0;break;case"full":case"body":a="data:image/jpeg;base64,"+D0;break;default:a=""}let n;if(typeof Image!="undefined")n=new Image;else if(re.Image)n=new re.Image;else{t(void 0);return}n.onload=async()=>{let r=En(n.naturalWidth,n.naturalHeight);if(!r)K("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(n,0,0);let i=await e.image(r,!0),o=i.tensor?await e.detect(i.tensor,e.config):void 0;t(o)}},a?n.src=a:t(void 0)})}async function ife(e){let t=r=>Buffer.from(r,"base64"),a;e.config.warmup==="face"?a=t(O0):a=t(D0);let n;if("node"in je&&ua()==="tensorflow"){let r=(void 0).decodeJpeg(a),s=Gt(r,0);e.tf.dispose(r),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&K("Warmup tfjs-node not loaded");return n}async function ofe(e){let t;return typeof createImageBitmap=="function"?t=await rfe(e):typeof Image!="undefined"||re.Canvas!==void 0?t=await sfe(e):t=await ife(e),t}async function lfe(e){var o,l,u,p;if(!V().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=ua(),a=tr();if(t!=="webgl"&&t!=="humangl"||!(a!=null&&a.checkCompileCompletion))return;V().set("ENGINE_COMPILE_ONLY",!0);let n=vt().state.numTensors,r=[];for(let[c,d]of Object.entries(e.models.models)){if(!d)continue;let h=d!=null&&d.modelSignature&&((l=(o=d==null?void 0:d.inputs)==null?void 0:o[0])!=null&&l.shape)?[...d.inputs[0].shape]:[1,64,64,3],f=d!=null&&d.modelSignature&&((p=(u=d==null?void 0:d.inputs)==null?void 0:u[0])!=null&&p.dtype)?d.inputs[0].dtype:"float32";for(let g=0;g<h.length;g++)h[g]===-1&&(h[g]=g===0?1:64);let m=mn(h,f);try{let g=d.execute(m);r.push(c),Array.isArray(g)?g.forEach(y=>J(y)):J(g)}catch(g){e.config.debug&&K("compile fail model:",c)}J(m)}let s=await a.checkCompileCompletionAsync();a.getUniformLocations(),e.config.debug&&K("compile pass:",{models:r,kernels:s.length}),V().set("ENGINE_COMPILE_ONLY",!1);let i=vt().state.numTensors;i-n>0&&K("tensor leak:",i-n)}async function Zw(e,t){await wp(e,!1);let a=ae();return e.state="warmup",t&&(e.config=Nt(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?lr():new Promise(async n=>{await e.models.load(),await lfe(e);let r=await ofe(e),s=ae();e.config.debug&&K("warmup",e.config.warmup,Math.round(s-a),"ms"),e.emit("warmup"),n(r)})}var Mu,$p,_p,z0,Ss,b5=class{constructor(t){de(this,"version");de(this,"config");de(this,"result");de(this,"state");de(this,"process");de(this,"tf");de(this,"env",re);de(this,"draw",e0);de(this,"match",N0);de(this,"models");de(this,"events");de(this,"faceTriangulation");de(this,"faceUVMap");de(this,"performance");Gn(this,Mu,void 0);Gn(this,$p,void 0);Gn(this,_p,void 0);de(this,"analyze",(...t)=>{if(!Ha(this,$p))return;let a=this.tf.engine().state.numTensors,n=Ha(this,Mu);mr(this,Mu,a);let r=a-n;r!==0&&K(...t,r)});Gn(this,z0,t=>{if(!Ha(this,_p))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof pt))return"input must be a tensor";try{this.tf.getBackend()}catch(a){return"backend not loaded"}return null});de(this,"webcam",new qh);de(this,"emit",t=>{var a;(a=this.events)!=null&&a.dispatchEvent&&this.events.dispatchEvent(new Event(t))});Gn(this,Ss,{});let a=(xp.tfjs||t3).replace(/-(.*)/,"");So.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${a}/dist/`,So.modelBasePath=re.browser?"../models/":"file://models/",this.version=K3,Object.defineProperty(this,"version",{value:K3}),this.config=JSON.parse(JSON.stringify(So)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Nt(this.config,t)),o9(this.config),this.tf=je,this.state="idle",mr(this,Mu,0),mr(this,$p,!1),mr(this,_p,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new Mp(this),ag(),this.result=lr(),this.process={tensor:null,canvas:null},this.faceTriangulation=ek,this.faceUVMap=tk,F0(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&K(`version: ${this.version}`),this.config.debug&&K(`tfjs version: ${this.tf.version["tfjs-core"]}`);let n=JSON.parse(JSON.stringify(this.env));delete n.kernels,delete n.initial,delete n.perfadd,this.config.debug&&K("environment:",n)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(So)),this.config.backend=t,q3(),re.initial=!0}validate(t){let a=G3(So,t||this.config);return a.length===0&&(this.config=Nt(this.config,t)),a}now(){return ae()}image(t,a=!1){return Hh(t,this.config,a)}async segmentation(t,a){var s,i,o;if(a&&(this.config=Nt(this.config,a)),!this.config.segmentation.enabled)return null;let n=await Hh(t,this.config);if(!n.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Hw(n.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("meet")&&(r=await ww(n.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("selfie")&&(r=await qw(n.tensor,this.config)),J(n.tensor),r}compare(t,a){return i9(this.config,t,a)}async init(){await wp(this,!0),await this.tf.ready(),q3()}async load(t){this.state="load";let a=ae(),n=Object.values(this.models.models).filter(i=>i).length;t&&(this.config=Nt(this.config,t)),this.env.initial&&(await wp(this,!1)||K("error: backend check failed"),await Jd(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&K("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(ae()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return kw(t,this.config)}async warmup(t){let a=ae(),n=await Zw(this,t),r=ae();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let l=Number(o.kernelTimeMs)||0;r[o.name]?r[o.name]+=l:r[o.name]=l,s+=l}let i=[];Object.entries(r).forEach(o=>i.push({kernel:o[0],time:o[1],perc:0}));for(let o of i)o.perc=Math.round(1e3*o.time/s)/1e3,o.time=Math.round(1e3*o.time)/1e3;return i.sort((o,l)=>l.time-o.time),i.length=20,i}async detect(t,a){return this.state="detect",new Promise(async n=>{var g,y,x,A,b,k,S,C,N,$,M,R,I,_,D,W,P,U,G,q,H;this.state="config";let r;this.config=Nt(this.config,a),this.state="check";let s=Ha(this,z0).call(this,t);s&&(K(s,t),this.emit("error"),n(lr(s)));let i=ae();await this.load(),r=ae(),this.state="image";let o=await Hh(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Get Image:"),!o.tensor){this.config.debug&&K("could not convert input to tensor"),this.emit("error"),n(lr("could not convert input to tensor"));return}this.emit("image"),r=ae(),this.config.skipAllowed=await s9(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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(ae()-r):Math.trunc(ae()-r),this.analyze("Check Changed:");let l=[],u=[],p=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Gg(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=ae(),l=this.config.face.enabled?await Gg(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),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 d=this.config.body.maxDetected===-1?Nt(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?f5(o.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?og(o.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?fg(o.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?o5(o.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ae(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await f5(o.tensor,d):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?u=this.config.body.enabled?await og(o.tensor,d):[]:(S=this.config.body.modelPath)!=null&&S.includes("efficientpose")?u=this.config.body.enabled?await fg(o.tensor,d):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(u=this.config.body.enabled?await o5(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Nt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(($=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&$.includes("handdetect")?p=this.config.hand.enabled?Kg(o.tensor,h):[]:(R=(M=this.config.hand.detector)==null?void 0:M.modelPath)!=null&&R.includes("handtrack")&&(p=this.config.hand.enabled?Jg(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ae(),(_=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?await Kg(o.tensor,h):[]:(W=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&W.includes("handtrack")&&(p=this.config.hand.enabled?await Jg(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((P=this.config.object.modelPath)!=null&&P.includes("nanodet")?c=this.config.object.enabled?u5(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?dg(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ae(),(G=this.config.object.modelPath)!=null&&G.includes("nanodet")?c=this.config.object.enabled?await u5(o.tensor,this.config):[]:(q=this.config.object.modelPath)!=null&&q.includes("centernet")&&(c=this.config.object.enabled?await dg(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ae(),f=[...Jk(l),...Yk(u),...ew(p),...Qk(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ae()-i):Math.trunc(ae()-i);let m=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:f,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:m[2],height:m[1],get persons(){return Kw(l,u,p,f,m)}},J(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(Ha(this,Ss)[t.id]||(this.config.debug&&K("video start",t.id),Ha(this,Ss)[t.id]=!0),!t.paused&&Ha(this,Ss)[t.id]&&t.readyState>=2&&await this.detect(t),n>0&&await this.sleep(n),Ha(this,Ss)[t.id]&&requestAnimationFrame(()=>this.video(t,a,n))):(this.config.debug&&K("video stop",t.id),Ha(this,Ss)[t.id]=!1)}};Mu=new WeakMap,$p=new WeakMap,_p=new WeakMap,z0=new WeakMap,Ss=new WeakMap;return FI(dfe);})();
diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js
index 8fb0fc0b..d62458d4 100644
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
-"use strict";var Wo=Object.create;var E2=Object.defineProperty;var Do=Object.getOwnPropertyDescriptor;var Fo=Object.getOwnPropertyNames;var Bo=Object.getPrototypeOf,Ho=Object.prototype.hasOwnProperty;var Go=(e,t,n)=>t in e?E2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Vo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var n in t)E2(e,n,{get:t[n],enumerable:!0})},j1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Fo(t))!Ho.call(e,r)&&r!==n&&E2(e,r,{get:()=>t[r],enumerable:!(o=Do(t,r))||o.enumerable});return e};var V=(e,t,n)=>(n=e!=null?Wo(Bo(e)):{},j1(t||!e||!e.__esModule?E2(n,"default",{value:e,enumerable:!0}):n,e)),Zo=e=>j1(E2({},"__esModule",{value:!0}),e);var k=(e,t,n)=>(Go(e,typeof t!="symbol"?t+"":t,n),n),I1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var q0=(e,t,n)=>(I1(e,t,"read from private field"),n?n.call(e):t.get(e)),ue=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member 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ha={};Se(ha,{Env:()=>z2,Human:()=>k1,default:()=>k1,defaults:()=>Ke,draw:()=>nt,empty:()=>Te,env:()=>R,match:()=>Ot,models:()=>P1});module.exports=Zo(ha);var ce=V(H());function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function O1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Kt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Kt(e[r],t[r],r,o);else{let s=e&&typeof 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Ke={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var C0=V(H());var I=V(H());var L1=`
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Ke={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var C0=G(H());var I=G(H());var O1=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +14,7 @@
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var C1=`
+`;var L1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +26,7 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,W1=`
+`,C1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +38,7 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,D1=`
+`,W1=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +51,7 @@
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,F1=`
+`,D1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +74,7 @@
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,B1=`
+`,F1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,8 +96,8 @@
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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W0={cacheModels:!0,cacheSupported:!0,verbose:!0,debug:!1,modelBasePath:""},S0={};async function ds(e,t){return W0.debug&&u("load model fetch:",e,t),fetch(e,t)}function V1(e){W0.cacheModels=e.cacheModels,W0.verbose=e.debug,W0.modelBasePath=e.modelBasePath}async function N(e){var c,x,i,f;let t=N1(W0.modelBasePath,e||"");t.toLowerCase().endsWith(".json")||(t+=".json");let n=t.includes("/")?t.split("/"):t.split("\\"),o=n[n.length-1].replace(".json",""),r="indexeddb://"+o;S0[o]={name:o,sizeFromManifest:0,sizeLoadedWeights:0,sizeDesired:$t[o],inCache:!1,url:""},W0.cacheSupported=typeof indexedDB!="undefined";let s={};try{s=W0.cacheSupported&&W0.cacheModels?await y2.io.listModels():{}}catch(d){W0.cacheSupported=!1}S0[o].inCache=W0.cacheSupported&&W0.cacheModels&&Object.keys(s).includes(r),S0[o].url=S0[o].inCache?r:t;let A=typeof fetch=="undefined"?{}:{fetchFunc:(d,m)=>ds(d,m)},a=new y2.GraphModel(S0[o].url,A),l=!1;try{a.findIOHandler(),W0.debug&&u("model load 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     [gender] [genderScore]%
     age: [age] years
@@ -118,7 +118,7 @@
     live: [live]%
     [emotions]
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t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let r=t.strides[o],s=Math.floor((e+r-1)/r),A=Math.floor((e+r-1)/r),a=t.anchors[o];for(let l=0;l<s;l++){let c=r*(l+.5);for(let x=0;x<A;x++){let i=r*(x+.5);for(let f=0;f<a;f++)n.push([i,c])}}}return n}function g3(e,t,n,o,r){let s=m2(t),A=e.map(d=>[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?h3(n,[0,0]):T5,c=a?A.map(d=>[...cA(d,l),d[2]]):A,x=a?lA(o):T5,i=lt(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function T3(e,t,n,o){let r=t.landmarks.length>=o5.count?o5.symmetryLine:_e.symmetryLine,s=0,A=T5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=aA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=lt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Pe.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=h3(-s,c),a=g5(t,i,[o,o]),Pe.dispose(i)}else a=g5(t,n,[o,o]);else 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t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,R5),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,p2),t.centersNormalized=L.div(t.centers,p2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,p2),t.endNormalized=L.mul(t.ends,p2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function P3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=L.image.resizeBilinear(e,[Ce,Ce]),n.div=L.div(n.resized,C.tf127),n.normalized=L.sub(n.div,C.tf05);let o=de==null?void 0:de.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((p,y)=>p.size-y.size);n.concat384=L.concat([d[0],d[2]],2),n.concat512=L.concat([d[1],d[3]],2),n.concat=L.concat([n.concat512,n.concat384],1),n.batch=L.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=L.squeeze(o[0]):n.batch=L.squeeze(o);L.dispose(o),n.boxes=yA(n.batch),n.logits=L.slice(n.batch,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.logits),n.scores=L.squeeze(n.sigmoid),n.nms=await L.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d<r.length;d++){let p=A[r[d]];if(p>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=L.slice(n.boxes,[r[d],0],[1,-1]),y.slice=L.slice(n.batch,[r[d],R3-1],[1,-1]),y.squeeze=L.squeeze(y.slice),y.landmarks=L.reshape(y.squeeze,[R3,-1]);let b=await y.bbox.data(),M={startPoint:[b[0],b[1]],endPoint:[b[2],b[3]],landmarks:await y.landmarks.array(),confidence:p},T=p3(M,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce]),m=xt(T,t.face.scale||xA),h=yt(m);h.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&h.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(h),Object.keys(y).forEach(S=>L.dispose(y[S]))}}return Object.keys(n).forEach(d=>L.dispose(n[d])),s}var xe=V(H());var U0,We=0,fA=2.3,M5=Ae.leftEyeLower0,P5=Ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function S3(e){var t,n;return R.initial&&(U0=null),U0?e.debug&&u("cached model:",U0.modelUrl):U0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=U0!=null&&U0.executor&&((n=U0.inputs)!=null&&n[0].shape)?U0.inputs[0].shape[2]:0,We===-1&&(We=64),U0}function ft(e,t,n,o){for(let r=0;r<r5.length;r++){let{key:s,indices:A}=r5[r],a=Ae[`${n}${s}`];if(!o||o.includes(s))for(let l=0;l<A.length;l++){let c=A[l];e[a[l]]=[t[c][0],t[c][1],(t[c][2]+e[a[l]][2])/2]}}}var mA=e=>{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},w3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(u3([e[n],e[o]]),fA)),a=m2(A),l=xe.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=xe.image.flipLeftRight(l);xe.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},E3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s<b2.numCoordinates;s++){let A=e[s*3],a=e[s*3+1],l=e[s*3+2];r.push([(o?1-A/We:A/We)*n[0]+t.startPoint[0],a/We*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(b2.index)}},z3=(e,t,n)=>{let o=e[Ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[Ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function j3(e,t,n){if(!(U0!=null&&U0.executor))return e;let{box:o,boxSize:r,crop:s}=w3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=w3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=xe.concat([s,l]);xe.dispose(s),xe.dispose(l);let x=U0.execute(c);xe.dispose(c);let i=await x.data();xe.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:p}=E3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:b,iris:M}=E3(y,A,a,!1),T=mA(e);Math.abs(T)<30?(ft(e,d,"left",null),ft(e,b,"right",null)):T<1?ft(e,d,"left",["EyeUpper0","EyeLower0"]):ft(e,b,"right",["EyeUpper0","EyeLower0"]);let m=z3(e,p,"left"),h=z3(e,M,"right");return e.concat(m).concat(h)}async function N3(e,t){var s,A,a,l,c,x,i,f,d,p;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((p=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:p.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,b)=>y+=e[b][2],0)/e2.length;for(let y=0;y<n.irisL.length/2;y++)e.push([n.irisL[2*y+0],n.irisL[2*y+1],o]);let r=t2.reduce((y,b)=>y+=e[b][2],0)/t2.length;for(let y=0;y<n.irisR.length/2;y++)e.push([n.irisR[2*y+0],n.irisR[2*y+1],r]);for(let y=0;y<n.eyeL.length/2;y++)e[e2[y]]=[n.eyeL[2*y+0],n.eyeL[2*y+1],e[e2[y]][2]];for(let y=0;y<n.eyeR.length/2;y++)e[t2[y]]=[n.eyeR[2*y+0],n.eyeR[2*y+1],e[t2[y]][2]];for(let y=0;y<n.lips.length/2;y++)e[L2[y]]=[n.lips[2*y+0],n.lips[2*y+1],e[L2[y]][2]];return e}var ge={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},r0=null,C2=0;async function O3(e,t){var l,c,x,i,f,d,p,y,b,M;let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>g()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await P3(e,t),ge.timestamp=g(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=C2;for(let T=0;T<ge.boxes.length;T++){let m=ge.boxes[T],h=0,S,P={id:A++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([h,S,P.tensor]=T3((x=t.face.detector)==null?void 0:x.rotation,m,e,(i=t.face.mesh)!=null&&i.enabled?C2:u2()),t.filter.equalization){let N=P.tensor?await Z2(P.tensor):void 0;De.dispose(P.tensor),N&&(P.tensor=N)}if(P.boxScore=Math.round(100*m.confidence)/100,!((f=t.face.mesh)!=null&&f.enabled)||!(r0!=null&&r0.executor)){P.box=ct(m,e),P.boxRaw=dt(m,e),P.score=P.boxScore,P.size=m.size,P.mesh=m.landmarks.map(N=>[(m.startPoint[0]+m.endPoint[0])/2+N[0]*e.shape[2]/u2(),(m.startPoint[1]+m.endPoint[1])/2+N[1]*e.shape[1]/u2()]),P.meshRaw=P.mesh.map(N=>[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&u("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),e0=await N.find(G=>G.shape[G.shape.length-1]===1).data();if(P.faceScore=Math.round(100*e0[0])/100,P.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1)){if(m.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(m,e),P.boxRaw=dt(m,e),P.score=P.boxScore,P.mesh=m.landmarks.map(G=>[(m.startPoint[0]+m.endPoint[0])/2+(m.endPoint[0]+m.startPoint[0])*G[0]/u2(),(m.startPoint[1]+m.endPoint[1])/2+(m.endPoint[1]+m.startPoint[1])*G[1]/u2()]),P.meshRaw=P.mesh.map(G=>[G[0]/(e.shape[2]||1),G[1]/(e.shape[1]||1),(G[2]||0)/a]);for(let G of Object.keys(_e))P.annotations[G]=[P.mesh[_e[G]]]}}else{let G=N.find(t0=>t0.shape[t0.shape.length-1]===1404),_=De.reshape(G,[-1,3]),A0=await _.array();De.dispose(_),(y=t.face.attention)!=null&&y.enabled?A0=await N3(A0,N):(b=t.face.iris)!=null&&b.enabled&&(A0=await j3(A0,P.tensor,C2)),P.mesh=g3(A0,m,h,S,C2),P.meshRaw=P.mesh.map(t0=>[t0[0]/(e.shape[2]||0),t0[1]/(e.shape[1]||0),(t0[2]||0)/a]);for(let t0 of Object.keys(Ae))P.annotations[t0]=Ae[t0].map(z0=>P.mesh[z0]);P.score=P.faceScore;let n0={...v3(P.mesh,m),confidence:m.confidence,landmarks:m.landmarks,size:m.size};P.box=ct(n0,e),P.boxRaw=dt(n0,e),s.push(n0)}De.dispose(N)}P.score>(((M=t.face.detector)==null?void 0:M.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function L3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&u("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await O(e.face.attention.modelPath):r0=await O((r=e.face.mesh)==null?void 0:r.modelPath),C2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var C3=$e,W3=O2;var Y0=V(H());var E5=[],M0,mt=[],D3=0,F3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function B3(e){var t,n,o;return R.initial&&(M0=null),M0?e.debug&&u("cached model:",M0.modelUrl):(M0=await O((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=M0==null?void 0:M0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),M0}async function S5(e,t,n,o){var A,a;if(!M0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-F3;return t.skipAllowed&&s&&r&&D3===o&&mt[n]&&mt[n].length>0?(w5++,mt[n]):(w5=0,new Promise(async l=>{var x;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let i={},f=M0!=null&&M0.inputs[0].shape?M0.inputs[0].shape[2]:0;if(t.face.emotion.crop>0){let p=t.face.emotion.crop,y=[[p,p,1-p,1-p]];i.resize=Y0.image.cropAndResize(e,y,[0],[f,f])}else i.resize=Y0.image.resizeBilinear(e,[f,f],!1);z5?(i.mul=Y0.mul(i.resize,255),i.normalize=Y0.sub(i.mul,[103.939,116.779,123.68]),i.emotion=M0==null?void 0:M0.execute(i.normalize)):(i.channels=Y0.mul(i.resize,C.rgb),i.grayscale=Y0.sum(i.channels,3,!0),i.grayscaleSub=Y0.sub(i.grayscale,C.tf05),i.grayscaleMul=Y0.mul(i.grayscaleSub,C.tf2),i.emotion=M0==null?void 0:M0.execute(i.grayscaleMul)),F3=g();let d=await i.emotion.data();for(let p=0;p<d.length;p++)d[p]>(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*d[p])/100),emotion:E5[p]});c.sort((p,y)=>y.score-p.score),Object.keys(i).forEach(p=>Y0.dispose(i[p]))}mt[n]=c,D3=o,l(c)}))}var ye=V(H());var j0,Fe=[],G3=0,V3=0,j5=Number.MAX_SAFE_INTEGER;async function Z3(e){var t;return R.initial&&(j0=null),j0?e.debug&&u("cached model:",j0.modelUrl):j0=await O((t=e.face.description)==null?void 0:t.modelPath),j0}function uA(e){let t=e.image||e.tensor||e;if(!(j0!=null&&j0.inputs[0].shape))return t;let n=ye.image.resizeBilinear(t,[j0.inputs[0].shape[2],j0.inputs[0].shape[1]],!1),o=ye.mul(n,C.tf255);return ye.dispose(n),o}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(j0!=null&&j0.executor))return r;let s=j5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>g()-G3;return t.skipAllowed&&s&&A&&V3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(j5++,Fe[n]):(j5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=uA(e),p=j0==null?void 0:j0.execute(d);G3=g(),ye.dispose(d);let b=await p.find(Z=>Z.shape[1]===1).data(),M=Math.trunc(200*Math.abs(b[0]-.5))/100;M>(t.face.description.minConfidence||0)&&(r.gender=b[0]<=.5?"female":"male",r.genderScore=Math.min(.99,M));let T=ye.argMax(p.find(Z=>Z.shape[1]===100),1),m=(await T.data())[0];ye.dispose(T);let S=await p.find(Z=>Z.shape[1]===100).data();r.age=Math.round(S[m-1]>S[m+1]?10*m-100*S[m-1]:10*m+100*S[m+1])/10,(Number.isNaN(b[0])||Number.isNaN(S[0]))&&u("faceres error:",{model:j0,result:p});let P=p.find(Z=>Z.shape[1]===1024),N=P?await P.data():[];r.descriptor=Array.from(N),p.forEach(Z=>ye.dispose(Z))}Fe[n]=r,V3=o,i(r)}))}var g2=.1,N5=.5;function hA(e,t,n){let o=!1,r=n.length-1;for(let s=0;s<n.length;r=s++)n[s].y>t!=n[r].y>t&&e<(n[r].x-n[s].x)*(t-n[s].y)/(n[r].y-n[s].y)+n[s].x&&(o=!o);return o}async function q3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),r=[];for(let A of Ae.silhouette)r.push({x:(e.mesh[A][0]-e.box[0])/e.box[2],y:(e.mesh[A][1]-e.box[1])/e.box[3]});g2&&g2>0&&(r=r.map(A=>({x:A.x>.5?A.x+g2:A.x-g2,y:A.y>.5?A.y+g2:A.y-g2})));for(let A=0;A<t;A++)for(let a=0;a<n;a++)hA(A/t,a/t,r)||(o.set(N5*o.get(0,a,A,0),0,a,A,0),o.set(N5*o.get(0,a,A,1),0,a,A,1),o.set(N5*o.get(0,a,A,2),0,a,A,2));return o.toTensor()}var ut=V(H());var P0,pt=[],O5=Number.MAX_SAFE_INTEGER,U3=0,Y3=0;async function K3(e){var t;return R.initial&&(P0=null),P0?e.debug&&u("cached model:",P0.modelUrl):P0=await O((t=e.face.antispoof)==null?void 0:t.modelPath),P0}async function L5(e,t,n,o){var A,a;if(!(P0!=null&&P0.executor))return 0;let r=(((A=t.face.antispoof)==null?void 0:A.skipTime)||0)>g()-Y3,s=O5<(((a=t.face.antispoof)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&U3===o&&pt[n]?(O5++,pt[n]):(O5=0,new Promise(async l=>{let 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i={age:0,gender:"unknown",genderScore:0,race:[]};(M=t.face.gear)!=null&&M.enabled&&([c.age,c.gender,c.race]=ae.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let m=0;m<d.length;m++)d[m]>(((T=t.face.gear)==null?void 0:T.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[m])/100,race:gA[m]});i.race.sort((m,h)=>h.score-m.score);let y=Array.from(await c.age.data()).map((m,h)=>[TA[h],m]).sort((m,h)=>h[1]-m[1]),b=y[0][0];for(let m=1;m<y.length;m++)b+=y[m][1]*(y[m][0]-b);i.age=Math.round(10*b)/10,Object.keys(c).forEach(m=>gt.dispose(c[m])),D5[n]=i,tn=o,nn=g(),l(i)}))}var T2=V(H());var K0,Tt=[],sn=0,An=0,H5=Number.MAX_SAFE_INTEGER;async function an(e){return R.initial&&(K0=null),K0?e.debug&&u("cached model:",K0.modelUrl):K0=await O(e.face.ssrnet.modelPathAge),K0}async function G5(e,t,n,o){var A,a,l,c;if(!K0)return{age:0};let 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A,a,l,c;if(!ie)return{gender:"unknown",genderScore:0};let r=V5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>g()-dn;return t.skipAllowed&&r&&s&&cn===o&&((l=vt[n])!=null&&l.gender)&&((c=vt[n])==null?void 0:c.genderScore)>0?(V5++,vt[n]):(V5=0,new Promise(async x=>{var p;if(!(ie!=null&&ie.inputs[0].shape))return;let i={};i.resize=I0.image.resizeBilinear(e,[ie.inputs[0].shape[2],ie.inputs[0].shape[1]],!1),i.enhance=I0.tidy(()=>{let[y,b,M]=I0.split(i.resize,3,3),T=I0.mul(y,Z5[0]),m=I0.mul(b,Z5[1]),h=I0.mul(M,Z5[2]),S=I0.addN([T,m,h]);return I0.mul(I0.sub(S,C.tf05),2)});let f={gender:"unknown",genderScore:0};(p=t.face.ssrnet)!=null&&p.enabled&&(i.gender=ie.execute(i.enhance));let d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(y=>I0.dispose(i[y])),vt[n]=f,cn=o,dn=g(),x(f)}))}var Rt=V(H());var J0,q5=[],fn=0,mn=0,pn=Number.MAX_SAFE_INTEGER;async function un(e){var t;return R.initial&&(J0=null),J0?e.debug&&u("cached model:",J0.modelUrl):J0=await O((t=e.face.mobilefacenet)==null?void 0:t.modelPath),J0}async function U5(e,t,n,o){var A,a;if(!(J0!=null&&J0.executor))return[];let r=pn<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>g()-mn;return t.skipAllowed&&s&&r&&fn===o&&q5[n]?(pn++,q5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(J0!=null&&J0.inputs[0].shape)){let i={};i.crop=Rt.image.resizeBilinear(e,[J0.inputs[0].shape[2],J0.inputs[0].shape[1]],!1),i.data=J0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>Rt.dispose(i[d]))}q5[n]=c,fn=o,mn=g(),l(c)})}var Mt=V(H());var Q0,Y5=[],bn=0,gn=0,Tn=Number.MAX_SAFE_INTEGER;async function vn(e){return R.initial&&(Q0=null),Q0?e.debug&&u("cached model:",Q0.modelUrl):Q0=await 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I5(d[w].tensor||l0.tensor([]),e.config,w,d.length),e.performance.description=R.perfadd?(e.performance.description||0)+Math.trunc(g()-n):Math.trunc(g()-n)),e.analyze("End Description:"),e.config.async&&([o,s,A,a,l,i,r,c,x]=await Promise.all([o,s,A,a,l,i,r,c,x])),e.analyze("Finish Face:"),(z0=e.config.face.ssrnet)!=null&&z0.enabled&&o&&s&&(i={...i,age:o.age,gender:s.gender,genderScore:s.genderScore}),(v=e.config.face.gear)!=null&&v.enabled&&r&&(i={...i,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),(T0=e.config.face.mobilefacenet)!=null&&T0.enabled&&a&&(i.descriptor=a),(L0=e.config.face.insightface)!=null&&L0.enabled&&l&&(i.descriptor=l);let X=(x0=e.config.face.iris)!=null&&x0.enabled?Pn(d[w],t.shape[2]):0,X0=(Y=e.config.face.detector)!=null&&Y.return?l0.squeeze(d[w].tensor):null;l0.dispose(d[w].tensor),d[w].tensor&&delete d[w].tensor;let y0={...d[w],id:w};i.age&&(y0.age=i.age),i.gender&&(y0.gender=i.gender),i.genderScore&&(y0.genderScore=i.genderScore),i.descriptor&&(y0.embedding=i.descriptor),i.race&&(y0.race=i.race),A&&(y0.emotion=A),c&&(y0.real=c),x&&(y0.live=x),X>0&&(y0.distance=X),B&&(y0.rotation=B),X0&&(y0.tensor=X0),f.push(y0),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),f};var B0={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=>B0.nameMapping[e],getPoints:e=>B0.pointsMapping[e]},He={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>He.nameMapping[e]},c0={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=>c0.nameMapping[e]},Be=class{constructor(t){k(this,"name");k(this,"curls");k(this,"directions");k(this,"weights");k(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,n,o){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,o])}direction(t,n,o){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,o])}weight(t,n){this.weights[t]=n;let o=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/o)}matchAgainst(t,n){let o=0;for(let r in t){let s=t[r],A=this.curls[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}for(let r in n){let s=n[r],A=this.directions[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}return o/10}};var{thumb:fe,index:ke,middle:we,ring:s2,pinky:A2}=B0,{none:me,half:MA,full:pe}=He,{verticalUp:v2,verticalDown:Ki,horizontalLeft:Q5,horizontalRight:PA,diagonalUpRight:kA,diagonalUpLeft:R2,diagonalDownRight:Ji,diagonalDownLeft:Qi}=c0,Ge=new Be("thumbs 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b=Math.sqrt(r*r+a*a),M=Math.sqrt(s*s+l*l),T=Math.sqrt(A*A+c*c),m=Math.max(b,M,T),h=e[0],S=e[1],P=n[0],N=n[1];m===b?(P=n[0],N=n[1]):m===T&&(h=t[0],S=t[1]);let G=zn([h,S],[P,N]),_=En(G,a2.TOTAL_ANGLE_VOTE_POWER);f+=_[0],d+=_[1],p+=_[2];for(let n0 of o){let t0=En(n0,a2.SINGLE_ANGLE_VOTE_POWER);f+=t0[0],d+=t0[1],p+=t0[2]}let A0;return f===Math.max(f,d,p)?A0=jn(l,a,c,i):p===Math.max(d,p)?A0=Sn(s,r,A,x):A0=zA(l,a,c,i,s,r,A,x),A0}function In(e){let t=[],n=[],o=[],r=[];if(!e)return{curls:o,directions:r};for(let s of B0.all){let A=B0.getPoints(s),a=[],l=[];for(let c of A){let x=e[c[0]],i=e[c[1]],f=zn(x,i),d=f[0],p=f[1];a.push(d),l.push(p)}t.push(a),n.push(l)}for(let s of B0.all){let A=s===B0.thumb?1:0,a=B0.getPoints(s),l=e[a[A][0]],c=e[a[A+1][1]],x=e[a[3][1]],i=EA(l,c,x),f=SA(l,c,x,t[s].slice(A));o[s]=i,r[s]=f}return{curls:o,directions:r}}function Pt(e){if(!e||e.length===0)return null;let t=In(e),n={};for(let o of 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t},Ln=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Cn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A<e.length;A++){if(!((o=(n=e[A].annotations)==null?void 0:n.leftEyeIris)!=null&&o[0])||!((s=(r=e[A].annotations)==null?void 0:r.rightEyeIris)!=null&&s[0]))continue;let a=e[A].annotations.leftEyeIris[3][0]-e[A].annotations.leftEyeIris[1][0],l=e[A].annotations.leftEyeIris[4][1]-e[A].annotations.leftEyeIris[2][1],c=Math.abs(a*l),x=e[A].annotations.rightEyeIris[3][0]-e[A].annotations.rightEyeIris[1][0],i=e[A].annotations.rightEyeIris[4][1]-e[A].annotations.rightEyeIris[2][1],f=Math.abs(x*i),d=!1;Math.abs(c-f)/Math.max(c,f)<.25&&(d=!0,t.push({iris:A,gesture:"facing center"}));let y=Math.abs(e[A].mesh[263][0]-e[A].annotations.leftEyeIris[0][0])/e[A].box[2],b=Math.abs(e[A].mesh[33][0]-e[A].annotations.rightEyeIris[0][0])/e[A].box[2];(y>.06||b>.06)&&(d=!1),y>b?y>.05&&t.push({iris:A,gesture:"looking right"}):b>.05&&t.push({iris:A,gesture:"looking left"});let 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kt(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function W2(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Hn(e,t,n){let o=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r]];return Bn.image.cropAndResize(t,s,[0],n)}function Gn(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function wt(e,t=1.5){let n=W2(e),o=kt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function Et(e){let t=W2(e),n=kt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function IA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Vn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return IA(n)}var Dn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}function NA(e,t){let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n}function Fn(e,t){let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let s=0;s<o;s++)n[r].push(Xe(e[r],NA(t,s)))}return n}function $5(e,t){let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=Dn(t[0],t[1]),A=Fn(s,r),a=Dn(-t[0],-t[1]);return Fn(A,a)}function Zn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-Xe(t[0],n),-Xe(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]}function e1(e,t){return[Xe(e,t[0]),Xe(e,t[1])]}var 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),p={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Gn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var _0=V(H());var CA=5,Un=1.65,Yn=[0,5,9,13,17,1,2],WA=0,DA=2,Kn=0,St=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return wt(Et(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=wt(Et(n),Un);o.palmLandmarks=[];for(let r=0;r<Yn.length;r++)o.palmLandmarks.push(t[Yn[r]].slice(0,2));return o}transformRawCoords(t,n,o,r){let s=kt(n),A=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],a=t.map(d=>[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=$5(o,[0,0]),c=a.map(d=>[...e1(d,l),d[2]]),x=Zn(r),i=[...W2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Kn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(c)if(n.hand.landmarks){let x=n.hand.rotation?Vn(c.palmLandmarks[WA],c.palmLandmarks[DA]):0,i=W2(c),f=[i[0]/t.shape[2],i[1]/t.shape[1]],d=n.hand.rotation&&R.kernels.includes("rotatewithoffset")?_0.image.rotateWithOffset(t,x,0,f):t.clone(),p=$5(-x,i),y=o?this.getBoxForPalmLandmarks(c.palmLandmarks,p):c,b=Hn(y,d,[this.inputSize,this.inputSize]),M=_0.div(b,C.tf255);_0.dispose(b),_0.dispose(d);let[T,m]=this.handPoseModel.execute(M);Kn=g(),_0.dispose(M);let h=(await T.data())[0];if(_0.dispose(T),h>=n.hand.minConfidence/4){let S=_0.reshape(m,[-1,3]),P=await S.array();_0.dispose(m),_0.dispose(S);let N=this.transformRawCoords(P,y,x,p),Z=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...Z,confidence:h};let e0={landmarks:N,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:Z.startPoint,bottomRight:Z.endPoint}};a.push(e0)}else this.storedBoxes[l]=null;_0.dispose(m)}else{let x=wt(Et(c),Un),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Jn={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]},i2,l2,Qn;async function t1(e,t){let n=await Qn.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let x of Object.keys(Jn))s[x]=Jn[x].map(i=>n[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]<a[0]&&(a[0]=x[0]),x[1]<a[1]&&(a[1]=x[1]),x[0]>a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Pt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function _n(e){var n,o;R.initial&&(i2=null,l2=null),!i2||!l2?[i2,l2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",i2.modelUrl),e.debug&&u("cached model:",l2.modelUrl));let t=i2?new zt(i2):void 0;return t&&l2&&(Qn=new St(t,l2)),[i2,l2]}var J=V(H());var p0=[null,null],BA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qe=[[0,0],[0,0]],HA=["hand","fist","pinch","point","face","tip","pinchtip"],eo=4,to=1.6,GA=512,VA=1.4,jt=Number.MAX_SAFE_INTEGER,n1=0,Ee=[0,0],m0={boxes:[],hands:[]},no={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 oo(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&u("cached model:",p0[0].modelUrl);else{J2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function ro(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&u("cached model:",p0[1].modelUrl);else{p0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function ZA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,GA),A=Math.round(s*r/8)*8;o.resize=J.image.resizeBilinear(e,[s,A]),o.cast=J.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,BA),o.boxes=J.squeeze(o.rawBoxes,[0,2]),o.scores=J.squeeze(o.rawScores,[0]);let a=J.unstack(o.scores,1);J.dispose(a[eo]),a.splice(eo,1),o.filtered=J.stack(a,1),J.dispose(a),o.max=J.max(o.filtered,1),o.argmax=J.argMax(o.filtered,1);let l=0;o.nms=await J.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=J.slice(o.boxes,f,1),p=await d.data();J.dispose(d);let y=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=st(y,VA),M=[Math.trunc(y[0]*Ee[0]),Math.trunc(y[1]*Ee[1]),Math.trunc(y[2]*Ee[0]),Math.trunc(y[3]*Ee[1])],T=x[f],m=HA[i[f]],h={id:l++,score:T,box:M,boxRaw:b,label:m};n.push(h)}return Object.keys(o).forEach(f=>J.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function o1(e,t,n){let o={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&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=J.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Pt(o.keypoints);for(let i of Object.keys(no))o.annotations[i]=no[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>J.dispose(r[l]))}return o}async function r1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],jt++;let n=(t.hand.skipTime||0)>g()-n1,o=jt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-n1,l=jt<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))):(m0.boxes=await ZA(e,t),n1=g(),m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))),jt=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x<m0.hands.length;x++){let i=t3(m0.hands[x].keypoints,Ee);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=st(i.box,to),d=st(i.boxRaw,to);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x<m0.hands.length;x++){let i=Me(m0.hands[x].keypoints,Ee);m0.hands[x].box=i.box,m0.hands[x].boxRaw=i.boxRaw}A(m0.hands)})}var Te=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var D2={};Se(D2,{connected:()=>Nt,horizontal:()=>s1,kpt:()=>It,relative:()=>a1,vertical:()=>A1});var It=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],s1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],A1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],a1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Nt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var E=Te(),i1=0;function Ao(e,t){var A,a,l,c,x,i,f,d,p,y,b,M,T,m,h,S,P,N,Z,e0,G,_,A0,n0,t0,z0;let n=g();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(E.canvas=e.canvas),e.error&&(E.error=e.error),!E.body||e.body.length!==E.body.length)E.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let T0=e.body[v].box.map((B,X)=>((r-1)*E.body[v].box[X]+B)/r),L0=e.body[v].boxRaw.map((B,X)=>((r-1)*E.body[v].boxRaw[X]+B)/r),x0=e.body[v].keypoints.map((B,X)=>{var X0,y0,ze,w2,d2,w1,E1,z1,S1;return{score:B.score,part:B.part,position:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[0]||0)+(B.position[0]||0))/r:B.position[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[1]||0)+(B.position[1]||0))/r:B.position[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[E.body[v].keypoints[X]?((r-1)*(((X0=E.body[v].keypoints[X].distance)==null?void 0:X0[0])||0)+(((y0=B.distance)==null?void 0:y0[0])||0))/r:(ze=B.distance)==null?void 0:ze[0],E.body[v].keypoints[X]?((r-1)*(((w2=E.body[v].keypoints[X].distance)==null?void 0:w2[1])||0)+(((d2=B.distance)==null?void 0:d2[1])||0))/r:(w1=B.distance)==null?void 0:w1[1],E.body[v].keypoints[X]?((r-1)*(((E1=E.body[v].keypoints[X].distance)==null?void 0:E1[2])||0)+(((z1=B.distance)==null?void 0:z1[2])||0))/r:(S1=B.distance)==null?void 0:S1[2]]}}),Y={},w={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?w=it:(a=t.body.modelPath)!=null&&a.includes("blazepose")?w=ot:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(w=D2);for(let[B,X]of Object.entries(w.connected)){let X0=[];for(let y0=0;y0<X.length-1;y0++){let ze=x0.find(d2=>d2.part===X[y0]),w2=x0.find(d2=>d2.part===X[y0+1]);ze&&w2&&X0.push([ze.position,w2.position])}Y[B]=X0}E.body[v]={...e.body[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.hand||e.hand.length!==E.hand.length)E.hand=JSON.parse(JSON.stringify(e.hand));else for(let v=0;v<e.hand.length;v++){let T0=e.hand[v].box.map((w,B)=>((r-1)*E.hand[v].box[B]+w)/r),L0=e.hand[v].boxRaw.map((w,B)=>((r-1)*E.hand[v].boxRaw[B]+w)/r);E.hand[v].keypoints.length!==e.hand[v].keypoints.length&&(E.hand[v].keypoints=e.hand[v].keypoints);let x0=e.hand[v].keypoints&&e.hand[v].keypoints.length>0?e.hand[v].keypoints.map((w,B)=>w.map((X,X0)=>((r-1)*(E.hand[v].keypoints[B][X0]||1)+(X||0))/r)):[],Y={};if(Object.keys(E.hand[v].annotations).length!==Object.keys(e.hand[v].annotations).length)E.hand[v].annotations=e.hand[v].annotations,Y=E.hand[v].annotations;else if(e.hand[v].annotations)for(let w of Object.keys(e.hand[v].annotations))Y[w]=(i=(x=(c=e.hand[v])==null?void 0:c.annotations)==null?void 0:x[w])!=null&&i[0]?e.hand[v].annotations[w].map((B,X)=>B.map((X0,y0)=>((r-1)*E.hand[v].annotations[w][X][y0]+X0)/r)):null;E.hand[v]={...e.hand[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.face||e.face.length!==E.face.length)E.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let T0=e.face[v].box.map((Y,w)=>((r-1)*E.face[v].box[w]+Y)/r),L0=e.face[v].boxRaw.map((Y,w)=>((r-1)*E.face[v].boxRaw[w]+Y)/r),x0=e.face[v].annotations;if(Object.keys(E.face[v].annotations).length!==Object.keys(e.face[v].annotations).length)E.face[v].annotations=e.face[v].annotations,x0=E.face[v].annotations;else if(e.face[v].annotations)for(let Y of Object.keys(e.face[v].annotations))x0[Y]=(p=(d=(f=e.face[v])==null?void 0:f.annotations)==null?void 0:d[Y])!=null&&p[0]?e.face[v].annotations[Y].map((w,B)=>w.map((X,X0)=>((r-1)*E.face[v].annotations[Y][B][X0]+X)/r)):null;if(e.face[v].rotation){let Y={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Y.matrix=(y=e.face[v].rotation)==null?void 0:y.matrix,Y.angle={roll:((r-1)*(((M=(b=E.face[v].rotation)==null?void 0:b.angle)==null?void 0:M.roll)||0)+(((m=(T=e.face[v].rotation)==null?void 0:T.angle)==null?void 0:m.roll)||0))/r,yaw:((r-1)*(((S=(h=E.face[v].rotation)==null?void 0:h.angle)==null?void 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n=(t.body.skipTime||0)>g()-c2.last,o=x1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?c2.bodies:new Promise(async s=>{let A={};x1=0,A.input=yo(e,Lt),A.res=b0==null?void 0:b0.execute(A.input),c2.last=g();let a=await A.res.array();c2.bodies=A.res.shape[2]===17?KA(a,t,e):JA(a,t,e);for(let l of c2.bodies)fo(l,[e.shape[2]||1,e.shape[1]||1]),xo(l.keypoints);Object.keys(A).forEach(l=>mo.dispose(A[l])),s(c2.bodies)})}var E0=V(H());var le,Ct=[],ho=0,f1=Number.MAX_SAFE_INTEGER,Dt=0,Wt=2.5;async function bo(e){if(!le||R.initial){le=await O(e.object.modelPath);let t=le!=null&&le.executor?Object.values(le.modelSignature.inputs):void 0;Dt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&u("cached model:",le.modelUrl);return le}async function QA(e,t,n){var c,x;let o=0,r=[],s=Dt;for(let i of[1,2,4]){let f=i*13,d=E0.squeeze(e.find(m=>m.shape[1]===f**2&&(m.shape[2]||0)===f2.length)),p=await 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B2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_A=B2.length,F2=B2.reduce((e,t,n)=>(e[t]=n,e),{}),$A=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],V7=$A.map(([e,t])=>[F2[e],F2[t]]),To=[["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 vo(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,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 Ro(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,x)=>({id:x,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:f,position:d})=>({score:i,part:f,position:[Math.trunc(d.x*A),Math.trunc(d.y*s)],positionRaw:[d.x/o,d.y/o]})),annotations:{}});return e.map((c,x)=>a(c,x))}var Ft=class{constructor(t,n){k(this,"priorityQueue");k(this,"numberOfElements");k(this,"getElementValue");this.priorityQueue=new 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A5=R.perfadd?A5+Math.round(g()-o):Math.round(g()-o),t.performance.draw=A5,s}function a5(){f0.faceLabels=Oe.face,f0.bodyLabels=Oe.body,f0.bodyPartLabels=Oe.bodyPart,f0.handLabels=Oe.hand,f0.fingerLabels=Oe.finger,f0.objectLabels=Oe.object,f0.gestureLabels=Oe.gesture}var V0=G(H());var nt={};Se(nt,{connected:()=>l5,kpt:()=>i5});var i5=["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"],l5={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 D=G(H());var re,n2=224,Q1,Js=5,ot=[8,16,32,32,32];function Qs(){let e=[],t=0;for(;t<Js;){let n=0,o=t;for(;o<ot.length&&ot[o]===ot[t];)n+=2,o++;let r=ot[t],s=Math.ceil(n2/r),A=Math.ceil(n2/r);for(let a=0;a<s;++a)for(let l=0;l<A;++l)for(let c=0;c<n;++c)e.push({x:(l+.5)/A,y:(a+.5)/s});t=o}Q1={x:D.tensor1d(e.map(n=>n.x)),y:D.tensor1d(e.map(n=>n.y))}}async function _1(e){if(R.initial&&(re=null),!re&&e.body.detector&&e.body.detector.modelPath){re=await N(e.body.detector.modelPath);let t=re!=null&&re.executor?Object.values(re.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&re&&u("cached model:",re.modelUrl);return Qs(),re}var J1=[5,5];function _s(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),J1[0]),A=D.mul(D.div(A,n2),J1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function $s(e,t,n,o){var c,x;let r=[],s={};s.boxes=_s(e,Q1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let f=a[i],d=l[i],m=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],y={score:f,boxRaw:d,box:m};r.push(y)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function $1(e,t,n){let o={};o.res=re==null?void 0:re.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await $s(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Me(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function e3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function rt(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var G0,d5=256,c5=Number.MAX_SAFE_INTEGER,eA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},At=[],Le=[[0,0],[0,0],[0,0],[0,0]],t3=0,n3=e=>1-1/(1+Math.exp(e)),r3=e=>_1(e);async function s3(e){if(R.initial&&(G0=null),G0)e.debug&&u("cached model:",G0.modelUrl);else{G0=await N(e.body.modelPath);let t=G0!=null&&G0.executor?Object.values(G0.modelSignature.inputs):void 0;d5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return G0}function o3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=V0.image.cropAndResize(e,[n],[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],l=[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];Le=[[0,0],a,l,[0,0]],o.pad=V0.pad(o.cropped||e,Le),o.resize=V0.image.resizeBilinear(o.pad,[t,t]),r=V0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=V0.image.resizeBilinear(o.cropped||e,[t,t]),r=V0.div(o.resize,C.tf255)):r=V0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>V0.dispose(o[a])),r}function tA(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+Le[2][0]+Le[2][1])/t[0]-Le[2][0]),Math.trunc(o.position[1]*(t[1]+Le[1][0]+Le[1][1])/t[1]-Le[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(n){let o=n[2]-n[0],r=n[3]-n[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+n[1],s.positionRaw[1]/o+n[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function nA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),s=e.find(a=>a.part==="rightWrist"),A=e.find(a=>a.part==="rightIndex");r.position[2]=((s.position[2]||0)+(A.position[2]||0))/2}async function oA(e,t,n){if(!(G0!=null&&G0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=G0==null?void 0:G0.execute(e,eA.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>V0.dispose(o[m]));let a=[],l=5;for(let m=0;m<s.length/l;m++){let y=n3(s[l*m+3]),b=n3(s[l*m+4]),M=Math.trunc(100*y*b*r)/100,T=[s[l*m+0]/d5,s[l*m+1]/d5,s[l*m+2]+0],p=[Math.trunc(n[0]*T[0]),Math.trunc(n[1]*T[1]),T[2]],h=[A[l*m+0],A[l*m+1],A[l*m+2]+0];a.push({part:i5[m],positionRaw:T,position:p,distance:h,score:M})}if(r<(t.body.minConfidence||0))return null;nA(a);let c=tA(a,n),x=c.map(m=>m.position),i=Me(x,[n[0],n[1]]),f={};for(let[m,y]of Object.entries(l5)){let b=[];for(let M=0;M<y.length-1;M++){let T=c.find(h=>h.part===y[M]),p=c.find(h=>h.part===y[M+1]);T&&p&&b.push([T.position,p.position])}f[m]=b}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:f}}async function x5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>g()-t3,r=c5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&At!==null)c5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=o3(e,224);l=await $1(c,t,n),V0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;c<l.length;c++){let x=o3(e,256,(a=l[c])==null?void 0:a.boxRaw);At.length=0;let i=await oA(x,t,n);V0.dispose(x),i&&(i.id=c,At.push(i))}t3=g(),c5=0}return At}var D0=G(H());var f2=[{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 Z0,o2=0,y5=[],a3=0,f5=Number.MAX_SAFE_INTEGER;async function i3(e){if(R.initial&&(Z0=null),Z0)e.debug&&u("cached model:",Z0.modelUrl);else{Z0=await N(e.object.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;o2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return Z0}async function rA(e,t,n){if(!e)return[];let o={},r=[],s=await e.array();o.squeeze=D0.squeeze(e);let A=D0.split(o.squeeze,6,1);o.stack=D0.stack([A[1],A[0],A[3],A[2]],1),o.boxes=D0.squeeze(o.stack),o.scores=D0.squeeze(A[4]),o.classes=D0.squeeze(A[5]),D0.dispose([e,...A]),o.nms=await D0.image.nonMaxSuppressionAsync(o.boxes,o.scores,n.object.maxDetected||0,n.object.iouThreshold,n.object.minConfidence||0);let a=await o.nms.data(),l=0;for(let c of Array.from(a)){let x=Math.trunc(100*s[0][c][4])/100,i=s[0][c][5];if(Number.isNaN(i))continue;let f=f2[i].label,[d,m]=[s[0][c][0]/o2,s[0][c][1]/o2],y=[d,m,s[0][c][2]/o2-d,s[0][c][3]/o2-m],b=[Math.trunc(y[0]*t[0]),Math.trunc(y[1]*t[1]),Math.trunc(y[2]*t[0]),Math.trunc(y[3]*t[1])];r.push({id:l++,score:x,class:i,label:f,box:b,boxRaw:y})}return Object.keys(o).forEach(c=>D0.dispose(o[c])),r}async function m5(e,t){if(!(Z0!=null&&Z0.executor))return[];let n=(t.object.skipTime||0)>g()-a3,o=f5<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&y5.length>0?(f5++,y5):(f5=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=D0.image.resizeBilinear(e,[o2,o2]),a=t.object.enabled?Z0==null?void 0:Z0.execute(A,["tower_0/detections"]):null;a3=g(),D0.dispose(A);let l=await rA(a,s,t);y5=l,r(l)}))}var K=G(H());var at={};Se(at,{connected:()=>u5,kpt:()=>p5});var p5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],u5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var i0,c3=0,F0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},h5=Number.MAX_SAFE_INTEGER;async function d3(e){return R.initial&&(i0=null),i0?e.debug&&u("cached model:",i0.modelUrl):i0=await N(e.body.modelPath),i0}async function sA(e,t){let[n,o]=e.shape,r=K.reshape(e,[o*n]),s=K.max(r,0),A=(await s.data())[0];if(A>t){let a=K.argMax(r,0),l=K.mod(a,n),c=(await l.data())[0],x=K.div(a,n),i=(await x.data())[0];return K.dispose([r,s,a,l,x]),[c,i,A]}return K.dispose([r,s]),[0,0,A]}async function b5(e,t){if(!(i0!=null&&i0.executor)||!(i0!=null&&i0.inputs[0].shape))return[];let n=(t.body.skipTime||0)>g()-c3,o=h5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(F0.keypoints).length>0?(h5++,[F0]):(h5=0,new Promise(async r=>{let s=K.tidy(()=>{var m,y;let i=K.image.resizeBilinear(e,[((m=i0==null?void 0:i0.inputs[0].shape)==null?void 0:m[2])||0,((y=i0==null?void 0:i0.inputs[0].shape)==null?void 0:y[1])||0],!1),f=K.mul(i,C.tf2);return K.sub(f,C.tf1)}),A;if(t.body.enabled&&(A=i0==null?void 0:i0.execute(s)),c3=g(),K.dispose(s),A){F0.keypoints.length=0;let i=K.squeeze(A);K.dispose(A);let f=K.unstack(i,2);K.dispose(i);for(let d=0;d<f.length;d++){let[m,y,b]=await sA(f[d],t.body.minConfidence);b>(t.body.minConfidence||0)&&F0.keypoints.push({score:Math.round(100*b)/100,part:p5[d],positionRaw:[m/i0.inputs[0].shape[2],y/i0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/i0.inputs[0].shape[2]),Math.round(e.shape[1]*y/i0.inputs[0].shape[1])]})}f.forEach(d=>K.dispose(d))}F0.score=F0.keypoints.reduce((i,f)=>f.score>i?f.score:i,0);let a=F0.keypoints.map(i=>i.position[0]),l=F0.keypoints.map(i=>i.position[1]);F0.box=[Math.min(...a),Math.min(...l),Math.max(...a)-Math.min(...a),Math.max(...l)-Math.min(...l)];let c=F0.keypoints.map(i=>i.positionRaw[0]),x=F0.keypoints.map(i=>i.positionRaw[1]);F0.boxRaw=[Math.min(...c),Math.min(...x),Math.max(...c)-Math.min(...c),Math.max(...x)-Math.min(...x)];for(let[i,f]of Object.entries(u5)){let d=[];for(let m=0;m<f.length-1;m++){let y=F0.keypoints.find(M=>M.part===f[m]),b=F0.keypoints.find(M=>M.part===f[m+1]);y&&b&&y.score>(t.body.minConfidence||0)&&b.score>(t.body.minConfidence||0)&&d.push([y.position,b.position])}F0.annotations[i]=d}r([F0])}))}var l0=G(H());var De=G(H());var O=G(H());var Pe=G(H());var m2=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],it=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],lt=(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],ct=(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],m3=(e,t,n)=>{let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.landmarks.map(A=>[(A[0]+n[0])*t[0],(A[1]+n[1])*t[1]]);return{startPoint:o,endPoint:r,landmarks:s,confidence:e.confidence}},g5=(e,t,n)=>{let o=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r],A=Pe.image.cropAndResize(t,[s],[0],n),a=Pe.div(A,C.tf255);return Pe.dispose(A),a},dt=(e,t)=>{let n=it(e),o=m2(e),r=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:o}},xt=e=>{let t=it(e),n=m2(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(n[0]),Math.round(n[1])]}},p3=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},T5=[[1,0,0],[0,1,0],[0,0,1]],AA=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),aA=(e,t)=>AA(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var y3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],r2=(e,t)=>{let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n},iA=(e,t)=>{let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n},f3=(e,t)=>{let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let s=0;s<o;s++)n[r].push(r2(e[r],iA(t,s)))}return n},u3=(e,t)=>{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=y3(t[0],t[1]),A=f3(s,r),a=y3(-t[0],-t[1]);return f3(A,a)},lA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-r2(t[0],n),-r2(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},cA=(e,t)=>[r2(e,t[0]),r2(e,t[1])];function h3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let r=t.strides[o],s=Math.floor((e+r-1)/r),A=Math.floor((e+r-1)/r),a=t.anchors[o];for(let l=0;l<s;l++){let c=r*(l+.5);for(let x=0;x<A;x++){let i=r*(x+.5);for(let f=0;f<a;f++)n.push([i,c])}}}return n}function b3(e,t,n,o,r){let s=m2(t),A=e.map(d=>[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?u3(n,[0,0]):T5,c=a?A.map(d=>[...cA(d,l),d[2]]):A,x=a?lA(o):T5,i=it(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function g3(e,t,n,o){let r=t.landmarks.length>=o5.count?o5.symmetryLine:_e.symmetryLine,s=0,A=T5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=aA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=it(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Pe.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=u3(-s,c),a=g5(t,i,[o,o]),Pe.dispose(i)}else a=g5(t,n,[o,o]);else a=g5(t,n,[o,o]);return[s,A,a]}var dA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},T3=(e,t)=>{let n=dA(e),o=m2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var v3=6,xA=1.4,de,yt=null,Ce=0,p2=null,R3=()=>Ce;async function M3(e){var t;return R.initial&&(de=null),de?e.debug&&u("cached model:",de.modelUrl):de=await N((t=e.face.detector)==null?void 0:t.modelPath),Ce=de.executor&&de.inputs[0].shape?de.inputs[0].shape[2]:256,p2=O.scalar(Ce,"int32"),yt=O.tensor2d(h3(Ce)),de}function yA(e){if(!yt||!p2)return O.zeros([0,0]);let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,yt),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,p2),t.centersNormalized=O.div(t.centers,p2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,p2),t.endNormalized=O.mul(t.ends,p2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function P3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=O.image.resizeBilinear(e,[Ce,Ce]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf05);let o=de==null?void 0:de.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=O.concat([d[0],d[2]],2),n.concat512=O.concat([d[1],d[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=O.squeeze(o[0]):n.batch=O.squeeze(o);O.dispose(o),n.boxes=yA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d<r.length;d++){let m=A[r[d]];if(m>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=O.slice(n.boxes,[r[d],0],[1,-1]),y.slice=O.slice(n.batch,[r[d],v3-1],[1,-1]),y.squeeze=O.squeeze(y.slice),y.landmarks=O.reshape(y.squeeze,[v3,-1]);let b=await y.bbox.data(),M={startPoint:[b[0],b[1]],endPoint:[b[2],b[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=O.slice(yt,[r[d],0],[1,2]);let T=await y.anchor.data(),p=m3(M,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],T),h=dt(p,t.face.scale||xA),S=xt(h);S.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&S.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(S),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var xe=G(H());var U0,We=0,fA=2.3,R5=Ae.leftEyeLower0,M5=Ae.rightEyeLower0,u2={leftBounds:[R5[0],R5[R5.length-1]],rightBounds:[M5[0],M5[M5.length-1]]},h2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function S3(e){var t,n;return R.initial&&(U0=null),U0?e.debug&&u("cached model:",U0.modelUrl):U0=await N((t=e.face.iris)==null?void 0:t.modelPath),We=U0!=null&&U0.executor&&((n=U0.inputs)!=null&&n[0].shape)?U0.inputs[0].shape[2]:0,We===-1&&(We=64),U0}function ft(e,t,n,o){for(let r=0;r<r5.length;r++){let{key:s,indices:A}=r5[r],a=Ae[`${n}${s}`];if(!o||o.includes(s))for(let l=0;l<A.length;l++){let c=A[l];e[a[l]]=[t[c][0],t[c][1],(t[c][2]+e[a[l]][2])/2]}}}var mA=e=>{let t=e[u2.leftBounds[0]][2],n=e[u2.rightBounds[0]][2];return t-n},w3=(e,t,n,o,r,s=!1)=>{let A=xt(dt(p3([e[n],e[o]]),fA)),a=m2(A),l=xe.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=xe.image.flipLeftRight(l);xe.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},E3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s<h2.numCoordinates;s++){let A=e[s*3],a=e[s*3+1],l=e[s*3+2];r.push([(o?1-A/We:A/We)*n[0]+t.startPoint[0],a/We*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(h2.index)}},z3=(e,t,n)=>{let o=e[Ae[`${n}EyeUpper0`][h2.upperCenter]][2],r=e[Ae[`${n}EyeLower0`][h2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function j3(e,t,n){if(!(U0!=null&&U0.executor))return e;let{box:o,boxSize:r,crop:s}=w3(e,t,u2.leftBounds[0],u2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=w3(e,t,u2.rightBounds[0],u2.rightBounds[1],n,!0),c=xe.concat([s,l]);xe.dispose(s),xe.dispose(l);let x=U0.execute(c);xe.dispose(c);let i=await x.data();xe.dispose(x);let f=i.slice(0,h2.numCoordinates*3),{rawCoords:d,iris:m}=E3(f,o,r,!0),y=i.slice(h2.numCoordinates*3),{rawCoords:b,iris:M}=E3(y,A,a,!1),T=mA(e);Math.abs(T)<30?(ft(e,d,"left",null),ft(e,b,"right",null)):T<1?ft(e,d,"left",["EyeUpper0","EyeLower0"]):ft(e,b,"right",["EyeUpper0","EyeLower0"]);let p=z3(e,m,"left"),h=z3(e,M,"right");return e.concat(p).concat(h)}async function N3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,b)=>y+=e[b][2],0)/e2.length;for(let y=0;y<n.irisL.length/2;y++)e.push([n.irisL[2*y+0],n.irisL[2*y+1],o]);let r=t2.reduce((y,b)=>y+=e[b][2],0)/t2.length;for(let y=0;y<n.irisR.length/2;y++)e.push([n.irisR[2*y+0],n.irisR[2*y+1],r]);for(let y=0;y<n.eyeL.length/2;y++)e[e2[y]]=[n.eyeL[2*y+0],n.eyeL[2*y+1],e[e2[y]][2]];for(let y=0;y<n.eyeR.length/2;y++)e[t2[y]]=[n.eyeR[2*y+0],n.eyeR[2*y+1],e[t2[y]][2]];for(let y=0;y<n.lips.length/2;y++)e[O2[y]]=[n.lips[2*y+0],n.lips[2*y+1],e[O2[y]][2]];return e}var ge={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},r0=null,L2=0;async function O3(e,t){var l,c,x,i,f,d,m,y,b,M;let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>g()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await P3(e,t),ge.timestamp=g(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=L2;for(let T=0;T<ge.boxes.length;T++){let p=ge.boxes[T],h=0,S,P={id:A++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([h,S,P.tensor]=g3((x=t.face.detector)==null?void 0:x.rotation,p,e,(i=t.face.mesh)!=null&&i.enabled?L2:R3()),t.filter.equalization){let L=P.tensor?await V2(P.tensor):void 0;De.dispose(P.tensor),L&&(P.tensor=L)}if(P.boxScore=Math.round(100*p.confidence)/100,!((f=t.face.mesh)!=null&&f.enabled)||!(r0!=null&&r0.executor)){P.box=lt(p,e),P.boxRaw=ct(p,e),P.score=P.boxScore,P.size=p.size,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(L=>[L[0]/(e.shape[2]||0),L[1]/(e.shape[1]||0),(L[2]||0)/a]);for(let L of Object.keys(_e))P.annotations[L]=[P.mesh[_e[L]]]}else if(!r0)t.debug&&u("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let L=r0.execute(P.tensor),e0=await L.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*e0[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=lt(p,e),P.boxRaw=ct(p,e),P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=L.find(t0=>t0.shape[t0.shape.length-1]===1404),_=De.reshape(Z,[-1,3]),A0=await _.array();De.dispose(_),(y=t.face.attention)!=null&&y.enabled?A0=await N3(A0,L):(b=t.face.iris)!=null&&b.enabled&&(A0=await j3(A0,P.tensor,L2)),P.mesh=b3(A0,p,h,S,L2),P.meshRaw=P.mesh.map(t0=>[t0[0]/(e.shape[2]||0),t0[1]/(e.shape[1]||0),(t0[2]||0)/a]);for(let t0 of Object.keys(Ae))P.annotations[t0]=Ae[t0].map(z0=>P.mesh[z0]);P.score=P.faceScore;let n0={...T3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=lt(n0,e),P.boxRaw=ct(n0,e),s.push(n0)}De.dispose(L)}P.score>(((M=t.face.detector)==null?void 0:M.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function L3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&u("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await N(e.face.attention.modelPath):r0=await N((r=e.face.mesh)==null?void 0:r.modelPath),L2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var C3=$e,W3=N2;var Y0=G(H());var w5=[],M0,mt=[],D3=0,F3=0,k5=Number.MAX_SAFE_INTEGER,E5=!1;async function B3(e){var t,n,o;return R.initial&&(M0=null),M0?e.debug&&u("cached model:",M0.modelUrl):(M0=await N((t=e.face.emotion)==null?void 0:t.modelPath),E5=((o=(n=M0==null?void 0:M0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,E5?w5=["angry","disgust","fear","happy","neutral","sad","surprise"]:w5=["angry","disgust","fear","happy","sad","surprise","neutral"]),M0}async function z5(e,t,n,o){var A,a;if(!M0)return[];let r=k5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-F3;return t.skipAllowed&&s&&r&&D3===o&&mt[n]&&mt[n].length>0?(k5++,mt[n]):(k5=0,new Promise(async l=>{var x;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let i={},f=M0!=null&&M0.inputs[0].shape?M0.inputs[0].shape[2]:0;if(t.face.emotion.crop>0){let m=t.face.emotion.crop,y=[[m,m,1-m,1-m]];i.resize=Y0.image.cropAndResize(e,y,[0],[f,f])}else 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0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>g()-nn;return t.skipAllowed&&s&&r&&tn===o&&W5[n]?(D5++,W5[n]):(D5=0,new Promise(async l=>{var M,T;if(!(ae!=null&&ae.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];c.resize=gt.image.cropAndResize(e,x,[0],[ae.inputs[0].shape[2],ae.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(M=t.face.gear)!=null&&M.enabled&&([c.age,c.gender,c.race]=ae.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let p=0;p<d.length;p++)d[p]>(((T=t.face.gear)==null?void 0:T.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[p])/100,race:gA[p]});i.race.sort((p,h)=>h.score-p.score);let y=Array.from(await c.age.data()).map((p,h)=>[TA[h],p]).sort((p,h)=>h[1]-p[1]),b=y[0][0];for(let p=1;p<y.length;p++)b+=y[p][1]*(y[p][0]-b);i.age=Math.round(10*b)/10,Object.keys(c).forEach(p=>gt.dispose(c[p])),W5[n]=i,tn=o,nn=g(),l(i)}))}var g2=G(H());var K0,Tt=[],sn=0,An=0,B5=Number.MAX_SAFE_INTEGER;async function an(e){return R.initial&&(K0=null),K0?e.debug&&u("cached model:",K0.modelUrl):K0=await N(e.face.ssrnet.modelPathAge),K0}async function H5(e,t,n,o){var A,a,l,c;if(!K0)return{age:0};let r=B5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>g()-An;return t.skipAllowed&&r&&s&&sn===o&&((l=Tt[n])!=null&&l.age)&&((c=Tt[n])==null?void 0:c.age)>0?(B5++,Tt[n]):(B5=0,new Promise(async x=>{var d;if(!(K0!=null&&K0.inputs)||!K0.inputs[0]||!K0.inputs[0].shape)return;let i={};i.resize=g2.image.resizeBilinear(e,[K0.inputs[0].shape[2],K0.inputs[0].shape[1]],!1),i.enhance=g2.mul(i.resize,C.tf255);let f={age:0};if((d=t.face.ssrnet)!=null&&d.enabled&&(i.age=K0.execute(i.enhance)),i.age){let m=await 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i={};i.crop=Rt.image.resizeBilinear(e,[J0.inputs[0].shape[2],J0.inputs[0].shape[1]],!1),i.data=J0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>Rt.dispose(i[d]))}X5[n]=c,fn=o,mn=g(),l(c)})}var Mt=G(H());var Q0,U5=[],bn=0,gn=0,Tn=Number.MAX_SAFE_INTEGER;async function vn(e){return R.initial&&(Q0=null),Q0?e.debug&&u("cached model:",Q0.modelUrl):Q0=await N(e.face.insightface.modelPath),Q0}async function Y5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=Tn<(((A=t.face.insightface)==null?void 0:A.skipFrames)||0),s=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>g()-gn;return t.skipAllowed&&s&&r&&bn===o&&U5[n]?(Tn++,U5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.insightface)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=Mt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await 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kt(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function C2(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Hn(e,t,n){let o=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r]];return Bn.image.cropAndResize(t,s,[0],n)}function Gn(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function wt(e,t=1.5){let n=C2(e),o=kt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function Et(e){let t=C2(e),n=kt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function IA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Vn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return IA(n)}var Dn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}function NA(e,t){let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n}function Fn(e,t){let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let s=0;s<o;s++)n[r].push(Xe(e[r],NA(t,s)))}return n}function _5(e,t){let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=Dn(t[0],t[1]),A=Fn(s,r),a=Dn(-t[0],-t[1]);return Fn(A,a)}function Zn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-Xe(t[0],n),-Xe(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]}function $5(e,t){return[Xe(e,t[0]),Xe(e,t[1])]}var 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),m={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Gn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var _0=G(H());var CA=5,Un=1.65,Yn=[0,5,9,13,17,1,2],WA=0,DA=2,Kn=0,St=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>$5([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return wt(Et(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=wt(Et(n),Un);o.palmLandmarks=[];for(let r=0;r<Yn.length;r++)o.palmLandmarks.push(t[Yn[r]].slice(0,2));return o}transformRawCoords(t,n,o,r){let s=kt(n),A=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],a=t.map(d=>[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=_5(o,[0,0]),c=a.map(d=>[...$5(d,l),d[2]]),x=Zn(r),i=[...C2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Kn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(c)if(n.hand.landmarks){let x=n.hand.rotation?Vn(c.palmLandmarks[WA],c.palmLandmarks[DA]):0,i=C2(c),f=[i[0]/t.shape[2],i[1]/t.shape[1]],d=n.hand.rotation&&R.kernels.includes("rotatewithoffset")?_0.image.rotateWithOffset(t,x,0,f):t.clone(),m=_5(-x,i),y=o?this.getBoxForPalmLandmarks(c.palmLandmarks,m):c,b=Hn(y,d,[this.inputSize,this.inputSize]),M=_0.div(b,C.tf255);_0.dispose(b),_0.dispose(d);let[T,p]=this.handPoseModel.execute(M);Kn=g(),_0.dispose(M);let h=(await T.data())[0];if(_0.dispose(T),h>=n.hand.minConfidence/4){let S=_0.reshape(p,[-1,3]),P=await S.array();_0.dispose(p),_0.dispose(S);let L=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(L);this.storedBoxes[l]={...V,confidence:h};let e0={landmarks:L,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(e0)}else this.storedBoxes[l]=null;_0.dispose(p)}else{let x=wt(Et(c),Un),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Jn={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]},i2,l2,Qn;async function e1(e,t){let n=await Qn.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let x of Object.keys(Jn))s[x]=Jn[x].map(i=>n[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]<a[0]&&(a[0]=x[0]),x[1]<a[1]&&(a[1]=x[1]),x[0]>a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Pt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function _n(e){var n,o;R.initial&&(i2=null,l2=null),!i2||!l2?[i2,l2]=await Promise.all([e.hand.enabled?N((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?N((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",i2.modelUrl),e.debug&&u("cached model:",l2.modelUrl));let t=i2?new zt(i2):void 0;return t&&l2&&(Qn=new St(t,l2)),[i2,l2]}var J=G(H());var p0=[null,null],BA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qe=[[0,0],[0,0]],HA=["hand","fist","pinch","point","face","tip","pinchtip"],eo=4,to=1.6,GA=512,VA=1.4,jt=Number.MAX_SAFE_INTEGER,t1=0,Ee=[0,0],m0={boxes:[],hands:[]},no={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 oo(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&u("cached model:",p0[0].modelUrl);else{K2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await N((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function ro(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&u("cached model:",p0[1].modelUrl);else{p0[1]=await N((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function ZA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,GA),A=Math.round(s*r/8)*8;o.resize=J.image.resizeBilinear(e,[s,A]),o.cast=J.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,BA),o.boxes=J.squeeze(o.rawBoxes,[0,2]),o.scores=J.squeeze(o.rawScores,[0]);let a=J.unstack(o.scores,1);J.dispose(a[eo]),a.splice(eo,1),o.filtered=J.stack(a,1),J.dispose(a),o.max=J.max(o.filtered,1),o.argmax=J.argMax(o.filtered,1);let l=0;o.nms=await J.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=J.slice(o.boxes,f,1),m=await d.data();J.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],b=rt(y,VA),M=[Math.trunc(y[0]*Ee[0]),Math.trunc(y[1]*Ee[1]),Math.trunc(y[2]*Ee[0]),Math.trunc(y[3]*Ee[1])],T=x[f],p=HA[i[f]],h={id:l++,score:T,box:M,boxRaw:b,label:p};n.push(h)}return Object.keys(o).forEach(f=>J.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function n1(e,t,n){let o={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&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=J.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Pt(o.keypoints);for(let i of Object.keys(no))o.annotations[i]=no[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>J.dispose(r[l]))}return o}async function o1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],jt++;let n=(t.hand.skipTime||0)>g()-t1,o=jt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-t1,l=jt<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>n1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>n1(e,x,t))):(m0.boxes=await ZA(e,t),t1=g(),m0.hands=await Promise.all(m0.boxes.map(x=>n1(e,x,t))),jt=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x<m0.hands.length;x++){let i=e3(m0.hands[x].keypoints,Ee);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=rt(i.box,to),d=rt(i.boxRaw,to);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x<m0.hands.length;x++){let i=Me(m0.hands[x].keypoints,Ee);m0.hands[x].box=i.box,m0.hands[x].boxRaw=i.boxRaw}A(m0.hands)})}var Te=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};Se(W2,{connected:()=>Nt,horizontal:()=>r1,kpt:()=>It,relative:()=>A1,vertical:()=>s1});var It=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],r1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],s1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],A1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Nt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var E=Te(),a1=0;function Ao(e,t){var A,a,l,c,x,i,f,d,m,y,b,M,T,p,h,S,P,L,V,e0,Z,_,A0,n0,t0,z0;let n=g();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(E.canvas=e.canvas),e.error&&(E.error=e.error),!E.body||e.body.length!==E.body.length)E.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let T0=e.body[v].box.map((B,X)=>((r-1)*E.body[v].box[X]+B)/r),L0=e.body[v].boxRaw.map((B,X)=>((r-1)*E.body[v].boxRaw[X]+B)/r),x0=e.body[v].keypoints.map((B,X)=>{var X0,y0,ze,k2,d2,k1,w1,E1,z1;return{score:B.score,part:B.part,position:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[0]||0)+(B.position[0]||0))/r:B.position[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[1]||0)+(B.position[1]||0))/r:B.position[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[E.body[v].keypoints[X]?((r-1)*(((X0=E.body[v].keypoints[X].distance)==null?void 0:X0[0])||0)+(((y0=B.distance)==null?void 0:y0[0])||0))/r:(ze=B.distance)==null?void 0:ze[0],E.body[v].keypoints[X]?((r-1)*(((k2=E.body[v].keypoints[X].distance)==null?void 0:k2[1])||0)+(((d2=B.distance)==null?void 0:d2[1])||0))/r:(k1=B.distance)==null?void 0:k1[1],E.body[v].keypoints[X]?((r-1)*(((w1=E.body[v].keypoints[X].distance)==null?void 0:w1[2])||0)+(((E1=B.distance)==null?void 0:E1[2])||0))/r:(z1=B.distance)==null?void 0:z1[2]]}}),Y={},w={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?w=at:(a=t.body.modelPath)!=null&&a.includes("blazepose")?w=nt:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(w=W2);for(let[B,X]of Object.entries(w.connected)){let X0=[];for(let y0=0;y0<X.length-1;y0++){let ze=x0.find(d2=>d2.part===X[y0]),k2=x0.find(d2=>d2.part===X[y0+1]);ze&&k2&&X0.push([ze.position,k2.position])}Y[B]=X0}E.body[v]={...e.body[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.hand||e.hand.length!==E.hand.length)E.hand=JSON.parse(JSON.stringify(e.hand));else for(let v=0;v<e.hand.length;v++){let T0=e.hand[v].box.map((w,B)=>((r-1)*E.hand[v].box[B]+w)/r),L0=e.hand[v].boxRaw.map((w,B)=>((r-1)*E.hand[v].boxRaw[B]+w)/r);E.hand[v].keypoints.length!==e.hand[v].keypoints.length&&(E.hand[v].keypoints=e.hand[v].keypoints);let x0=e.hand[v].keypoints&&e.hand[v].keypoints.length>0?e.hand[v].keypoints.map((w,B)=>w.map((X,X0)=>((r-1)*(E.hand[v].keypoints[B][X0]||1)+(X||0))/r)):[],Y={};if(Object.keys(E.hand[v].annotations).length!==Object.keys(e.hand[v].annotations).length)E.hand[v].annotations=e.hand[v].annotations,Y=E.hand[v].annotations;else if(e.hand[v].annotations)for(let w of Object.keys(e.hand[v].annotations))Y[w]=(i=(x=(c=e.hand[v])==null?void 0:c.annotations)==null?void 0:x[w])!=null&&i[0]?e.hand[v].annotations[w].map((B,X)=>B.map((X0,y0)=>((r-1)*E.hand[v].annotations[w][X][y0]+X0)/r)):null;E.hand[v]={...e.hand[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.face||e.face.length!==E.face.length)E.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let T0=e.face[v].box.map((Y,w)=>((r-1)*E.face[v].box[w]+Y)/r),L0=e.face[v].boxRaw.map((Y,w)=>((r-1)*E.face[v].boxRaw[w]+Y)/r),x0=e.face[v].annotations;if(Object.keys(E.face[v].annotations).length!==Object.keys(e.face[v].annotations).length)E.face[v].annotations=e.face[v].annotations,x0=E.face[v].annotations;else if(e.face[v].annotations)for(let Y of Object.keys(e.face[v].annotations))x0[Y]=(m=(d=(f=e.face[v])==null?void 0:f.annotations)==null?void 0:d[Y])!=null&&m[0]?e.face[v].annotations[Y].map((w,B)=>w.map((X,X0)=>((r-1)*E.face[v].annotations[Y][B][X0]+X)/r)):null;if(e.face[v].rotation){let Y={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Y.matrix=(y=e.face[v].rotation)==null?void 0:y.matrix,Y.angle={roll:((r-1)*(((M=(b=E.face[v].rotation)==null?void 0:b.angle)==null?void 0:M.roll)||0)+(((p=(T=e.face[v].rotation)==null?void 0:T.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((S=(h=E.face[v].rotation)==null?void 0:h.angle)==null?void 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n=(t.body.skipTime||0)>g()-c2.last,o=d1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?c2.bodies:new Promise(async s=>{let A={};d1=0,A.input=yo(e,Lt),A.res=b0==null?void 0:b0.execute(A.input),c2.last=g();let a=await A.res.array();c2.bodies=A.res.shape[2]===17?KA(a,t,e):JA(a,t,e);for(let l of c2.bodies)fo(l,[e.shape[2]||1,e.shape[1]||1]),xo(l.keypoints);Object.keys(A).forEach(l=>mo.dispose(A[l])),s(c2.bodies)})}var E0=G(H());var le,Ct=[],ho=0,y1=Number.MAX_SAFE_INTEGER,Dt=0,Wt=2.5;async function bo(e){if(!le||R.initial){le=await N(e.object.modelPath);let t=le!=null&&le.executor?Object.values(le.modelSignature.inputs):void 0;Dt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&u("cached model:",le.modelUrl);return le}async function QA(e,t,n){var c,x;let o=0,r=[],s=Dt;for(let i of[1,2,4]){let f=i*13,d=E0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)===f2.length)),m=await d.array(),y=E0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)<f2.length)),b=E0.reshape(y,[-1,4,(((c=y.shape)==null?void 0:c[1])||0)/4]),M=E0.argMax(b,2),T=await M.array();for(let p=0;p<d.shape[0];p++)for(let h=0;h<(((x=d.shape)==null?void 0:x[1])||0);h++){let S=m[p][h];if(S>(n.object.minConfidence||0)&&h!==61){let P=(.5+Math.trunc(p%f))/f,L=(.5+Math.trunc(p/f))/f,V=T[p].map(v=>v*(f/i/s)),[e0,Z]=[P-Wt/i*V[0],L-Wt/i*V[1]],[_,A0]=[P+Wt/i*V[2]-e0,L+Wt/i*V[3]-Z],n0=[e0,Z,_,A0];n0=n0.map(v=>Math.max(0,Math.min(v,1)));let t0=[n0[0]*t[0],n0[1]*t[1],n0[2]*t[0],n0[3]*t[1]],z0={id:o++,score:Math.round(100*S)/100,class:h+1,label:f2[h].label,box:t0.map(v=>Math.trunc(v)),boxRaw:n0};r.push(z0)}}E0.dispose([d,y,b,M])}let A=r.map(i=>[i.boxRaw[1],i.boxRaw[0],i.boxRaw[3],i.boxRaw[2]]),a=r.map(i=>i.score),l=[];if(A&&A.length>0){let i=await E0.image.nonMaxSuppressionAsync(A,a,n.object.maxDetected||0,n.object.iouThreshold,n.object.minConfidence);l=Array.from(await i.data()),E0.dispose(i)}return r=r.filter((i,f)=>l.includes(f)).sort((i,f)=>f.score-i.score),r}async function f1(e,t){if(!(le!=null&&le.executor))return[];let n=(t.object.skipTime||0)>g()-ho,o=y1<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&Ct.length>0?(y1++,Ct):(y1=0,!R.kernels.includes("mod")||!R.kernels.includes("sparsetodense")?Ct:new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=E0.image.resizeBilinear(e,[Dt,Dt],!1),a=E0.div(A,C.tf255),l=E0.transpose(a,[0,3,1,2]),c;t.object.enabled&&(c=le.execute(l)),ho=g();let x=await QA(c,s,t);Ct=x,E0.dispose([A,a,l,...c]),r(x)}))}var H0=G(H());var F2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_A=F2.length,D2=F2.reduce((e,t,n)=>(e[t]=n,e),{}),$A=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Vi=$A.map(([e,t])=>[D2[e],D2[t]]),To=[["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 vo(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,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 Ro(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,x)=>({id:x,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:f,position:d})=>({score:i,part:f,position:[Math.trunc(d.x*A),Math.trunc(d.y*s)],positionRaw:[d.x/o,d.y/o]})),annotations:{}});return e.map((c,x)=>a(c,x))}var Ft=class{constructor(t,n){k(this,"priorityQueue");k(this,"numberOfElements");k(this,"getElementValue");this.priorityQueue=new 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d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function ma(e){let t;return typeof createImageBitmap=="function"?t=await xa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ya(e):t=await fa(e),t}async function pa(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],p=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let b=0;b<d.length;b++)d[b]===-1&&(d[b]=b===0?1:64);let y=d0.zeros(d,p);try{let b=f.execute(y);r.push(i),Array.isArray(b)?b.forEach(M=>d0.dispose(M)):d0.dispose(b)}catch(b){e.config.debug&&u("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&u("tensor leak:",A-o)}async function Co(e,t){await N2(e,!1);let n=g();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await pa(e);let r=await ma(e),s=g();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var k2,G2,V2,Zt,Ye,k1=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env",R);k(this,"draw",nt);k(this,"match",Ot);k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");ue(this,k2,void 0);ue(this,G2,void 0);ue(this,V2,void 0);k(this,"analyze",(...t)=>{if(!q0(this,G2))return;let n=this.tf.engine().state.numTensors,o=q0(this,k2);Re(this,k2,n);let r=n-o;r!==0&&u(...t,r)});ue(this,Zt,t=>{if(!q0(this,V2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ce.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"webcam",new K2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(ce.version.tfjs||ce.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=e5,Object.defineProperty(this,"version",{value:e5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),Z1(this.config),this.tf=ce,this.state="idle",Re(this,k2,0),Re(this,G2,!1),Re(this,V2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2(this),a5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=C3,this.faceUVMap=W3,Ht(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,_t(),R.initial=!0}validate(t){let n=Kt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return U2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await U2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await jo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await ao(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await No(o.tensor,this.config)),ce.dispose(o.tensor),r}compare(t,n){return V1(this.config,t,n)}async init(){await N2(this,!0),await this.tf.ready(),_t()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await N2(this,!1)||u("error: backend check failed"),await ce.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return Ao(t,this.config)}async warmup(t){let n=g(),o=await Co(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,M,T,m,h,S,P,N,Z,e0,G,_,A0,n0,t0,z0,v,T0,L0,x0,Y;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=q0(this,Zt).call(this,t);s&&(u(s,t),this.emit("error"),o(Te(s)));let A=g();await this.load(),r=g(),this.state="image";let a=await U2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=g(),this.config.skipAllowed=await G1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?J5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await J5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),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 f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?g1(a.tensor,f):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?x5(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?c=this.config.body.enabled?b5(a.tensor,f):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?y1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=g(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?await g1(a.tensor,f):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?c=this.config.body.enabled?await x5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await b5(a.tensor,f):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(c=this.config.body.enabled?await y1(a.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((e0=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&e0.includes("handdetect")?x=this.config.hand.enabled?t1(a.tensor,d):[]:(_=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&_.includes("handtrack")&&(x=this.config.hand.enabled?r1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(n0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&n0.includes("handdetect")?x=this.config.hand.enabled?await t1(a.tensor,d):[]:(z0=(t0=this.config.hand.detector)==null?void 0:t0.modelPath)!=null&&z0.includes("handtrack")&&(x=this.config.hand.enabled?await r1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((v=this.config.object.modelPath)!=null&&v.includes("nanodet")?i=this.config.object.enabled?m1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?m5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(L0=this.config.object.modelPath)!=null&&L0.includes("nanodet")?i=this.config.object.enabled?await m1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await m5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(r=g(),p=[...Ln(l),...On(c),...Wn(x),...Cn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let y=((Y=this.process.tensor)==null?void 0:Y.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:p,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Lo(l,c,x,p,y)}},ce.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(q0(this,Ye)[t.id]||(this.config.debug&&u("video start",t.id),q0(this,Ye)[t.id]=!0),!t.paused&&q0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),q0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),q0(this,Ye)[t.id]=!1)}};k2=new WeakMap,G2=new WeakMap,V2=new WeakMap,Zt=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models});
+2Q==`;async function xa(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(A=>A.blob()),n,o;switch(e.config.warmup){case"face":n=await t(Gt);break;case"body":case"full":n=await t(Vt);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function ya(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+Gt;break;case"full":case"body":n="data:image/jpeg;base64,"+Vt;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(R.Image)o=new R.Image;else{t(void 0);return}o.onload=async()=>{let r=se(o.naturalWidth,o.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function fa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Gt):n=t(Vt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function ma(e){let t;return typeof createImageBitmap=="function"?t=await xa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ya(e):t=await fa(e),t}async function pa(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],m=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let b=0;b<d.length;b++)d[b]===-1&&(d[b]=b===0?1:64);let y=d0.zeros(d,m);try{let b=f.execute(y);r.push(i),Array.isArray(b)?b.forEach(M=>d0.dispose(M)):d0.dispose(b)}catch(b){e.config.debug&&u("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&u("tensor leak:",A-o)}async function Co(e,t){await I2(e,!1);let n=g();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await pa(e);let r=await ma(e),s=g();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,H2,G2,Zt,Ye,P1=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env",R);k(this,"draw",tt);k(this,"match",Ot);k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");ue(this,P2,void 0);ue(this,H2,void 0);ue(this,G2,void 0);k(this,"analyze",(...t)=>{if(!q0(this,H2))return;let n=this.tf.engine().state.numTensors,o=q0(this,P2);Re(this,P2,n);let r=n-o;r!==0&&u(...t,r)});ue(this,Zt,t=>{if(!q0(this,G2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ce.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"webcam",new Y2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(ce.version.tfjs||ce.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=e5,Object.defineProperty(this,"version",{value:e5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),V1(this.config),this.tf=ce,this.state="idle",Re(this,P2,0),Re(this,H2,!1),Re(this,G2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new B2(this),a5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=C3,this.faceUVMap=W3,Ht(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,_t(),R.initial=!0}validate(t){let n=Kt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return q2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await q2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await jo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await ao(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await No(o.tensor,this.config)),ce.dispose(o.tensor),r}compare(t,n){return G1(this.config,t,n)}async init(){await I2(this,!0),await this.tf.ready(),_t()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await I2(this,!1)||u("error: backend check failed"),await ce.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return Ao(t,this.config)}async warmup(t){let n=g(),o=await Co(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,M,T,p,h,S,P,L,V,e0,Z,_,A0,n0,t0,z0,v,T0,L0,x0,Y;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=q0(this,Zt).call(this,t);s&&(u(s,t),this.emit("error"),o(Te(s)));let A=g();await this.load(),r=g(),this.state="image";let a=await q2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=g(),this.config.skipAllowed=await H1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?K5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await K5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),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 f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?b1(a.tensor,f):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?x5(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?c=this.config.body.enabled?b5(a.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?x1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=g(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?await b1(a.tensor,f):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?c=this.config.body.enabled?await x5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await b5(a.tensor,f):[]:(L=this.config.body.modelPath)!=null&&L.includes("movenet")&&(c=this.config.body.enabled?await x1(a.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((e0=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&e0.includes("handdetect")?x=this.config.hand.enabled?e1(a.tensor,d):[]:(_=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&_.includes("handtrack")&&(x=this.config.hand.enabled?o1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(n0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&n0.includes("handdetect")?x=this.config.hand.enabled?await e1(a.tensor,d):[]:(z0=(t0=this.config.hand.detector)==null?void 0:t0.modelPath)!=null&&z0.includes("handtrack")&&(x=this.config.hand.enabled?await o1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((v=this.config.object.modelPath)!=null&&v.includes("nanodet")?i=this.config.object.enabled?f1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?m5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(L0=this.config.object.modelPath)!=null&&L0.includes("nanodet")?i=this.config.object.enabled?await f1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await m5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=g(),m=[...Ln(l),...On(c),...Wn(x),...Cn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let y=((Y=this.process.tensor)==null?void 0:Y.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Lo(l,c,x,m,y)}},ce.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(q0(this,Ye)[t.id]||(this.config.debug&&u("video start",t.id),q0(this,Ye)[t.id]=!0),!t.paused&&q0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),q0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),q0(this,Ye)[t.id]=!1)}};P2=new WeakMap,H2=new WeakMap,G2=new WeakMap,Zt=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models});
diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js
index 26c83c92..a66f17ae 100644
--- a/dist/human.node-wasm.js
+++ b/dist/human.node-wasm.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
-"use strict";var Do=Object.create;var E2=Object.defineProperty;var Fo=Object.getOwnPropertyDescriptor;var Bo=Object.getOwnPropertyNames;var Ho=Object.getPrototypeOf,Go=Object.prototype.hasOwnProperty;var Vo=(e,t,n)=>t in e?E2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Zo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var n in t)E2(e,n,{get:t[n],enumerable:!0})},I1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Bo(t))!Go.call(e,r)&&r!==n&&E2(e,r,{get:()=>t[r],enumerable:!(o=Fo(t,r))||o.enumerable});return e};var V=(e,t,n)=>(n=e!=null?Do(Ho(e)):{},I1(t||!e||!e.__esModule?E2(n,"default",{value:e,enumerable:!0}):n,e)),Xo=e=>I1(E2({},"__esModule",{value:!0}),e);var k=(e,t,n)=>(Vo(e,typeof t!="symbol"?t+"":t,n),n),N1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var q0=(e,t,n)=>(N1(e,t,"read from private field"),n?n.call(e):t.get(e)),ue=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},Re=(e,t,n,o)=>(N1(e,t,"write to private field"),o?o.call(e,n):t.set(e,n),n);var H=Zo((ga,S2)=>{"use strict";var Yt=Object.defineProperty,qo=Object.getOwnPropertyDescriptor,Uo=Object.getOwnPropertyNames,Yo=Object.prototype.hasOwnProperty,Ko=(e,t)=>{for(var n in t)Yt(e,n,{get:t[n],enumerable:!0})},Ut=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Uo(t))!Yo.call(e,r)&&r!==n&&Yt(e,r,{get:()=>t[r],enumerable:!(o=qo(t,r))||o.enumerable});return e},Kt=(e,t,n)=>(Ut(e,t,"default"),n&&Ut(n,t,"default")),Jo=e=>Ut(Yt({},"__esModule",{value:!0}),e),z2={};Ko(z2,{version:()=>nr});S2.exports=Jo(z2);Kt(z2,require("@tensorflow/tfjs-core"),S2.exports);Kt(z2,require("@tensorflow/tfjs-converter"),S2.exports);Kt(z2,require("@tensorflow/tfjs-backend-wasm"),S2.exports);var O1="4.2.0",Qo="4.2.0",_o="4.2.0",$o="4.2.0",er="4.2.0",tr="0.0.1-alpha.17",nr={tfjs:O1,"tfjs-core":O1,"tfjs-converter":Qo,"tfjs-backend-cpu":_o,"tfjs-backend-webgl":$o,"tfjs-backend-wasm":er,"tfjs-backend-webgpu":tr}});var ha={};Se(ha,{Env:()=>j2,Human:()=>w1,default:()=>w1,defaults:()=>Ke,draw:()=>rt,empty:()=>Te,env:()=>R,match:()=>Ct,models:()=>k1});module.exports=Xo(ha);var ce=V(H());function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function L1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Jt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Jt(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function a0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let s=n[r],A=o[r];Array.isArray(s)&&Array.isArray(A)?n[r]=s.concat(...A):t(s)&&t(A)?n[r]=a0(s,A):n[r]=A}),n),{})}var Ke={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var C0=V(H());var I=V(H());var C1=`
+"use strict";var Do=Object.create;var w2=Object.defineProperty;var Fo=Object.getOwnPropertyDescriptor;var Bo=Object.getOwnPropertyNames;var Ho=Object.getPrototypeOf,Go=Object.prototype.hasOwnProperty;var Vo=(e,t,n)=>t in e?w2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Zo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var n in t)w2(e,n,{get:t[n],enumerable:!0})},j1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Bo(t))!Go.call(e,r)&&r!==n&&w2(e,r,{get:()=>t[r],enumerable:!(o=Fo(t,r))||o.enumerable});return e};var G=(e,t,n)=>(n=e!=null?Do(Ho(e)):{},j1(t||!e||!e.__esModule?w2(n,"default",{value:e,enumerable:!0}):n,e)),Xo=e=>j1(w2({},"__esModule",{value:!0}),e);var k=(e,t,n)=>(Vo(e,typeof t!="symbol"?t+"":t,n),n),I1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var q0=(e,t,n)=>(I1(e,t,"read from private field"),n?n.call(e):t.get(e)),ue=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member 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N1="4.2.0",Qo="4.2.0",_o="4.2.0",$o="4.2.0",er="4.2.0",tr="0.0.1-alpha.17",nr={tfjs:N1,"tfjs-core":N1,"tfjs-converter":Qo,"tfjs-backend-cpu":_o,"tfjs-backend-webgl":$o,"tfjs-backend-wasm":er,"tfjs-backend-webgpu":tr}});var ha={};Se(ha,{Env:()=>S2,Human:()=>k1,default:()=>k1,defaults:()=>Ke,draw:()=>ot,empty:()=>Te,env:()=>R,match:()=>Ct,models:()=>P1});module.exports=Xo(ha);var ce=G(H());function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function O1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Jt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Jt(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function a0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let s=n[r],A=o[r];Array.isArray(s)&&Array.isArray(A)?n[r]=s.concat(...A):t(s)&&t(A)?n[r]=a0(s,A):n[r]=A}),n),{})}var Ke={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var C0=G(H());var I=G(H());var L1=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +14,7 @@
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var W1=`
+`;var C1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +26,7 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,D1=`
+`,W1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +38,7 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,F1=`
+`,D1=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +51,7 @@
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,B1=`
+`,F1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +74,7 @@
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,H1=`
+`,B1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,8 +96,8 @@
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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     [gender] [genderScore]%
     age: [age] years
@@ -118,7 +118,7 @@
     live: [live]%
     [emotions]
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t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let r=t.strides[o],s=Math.floor((e+r-1)/r),A=Math.floor((e+r-1)/r),a=t.anchors[o];for(let l=0;l<s;l++){let c=r*(l+.5);for(let x=0;x<A;x++){let i=r*(x+.5);for(let f=0;f<a;f++)n.push([i,c])}}}return n}function T3(e,t,n,o,r){let s=m2(t),A=e.map(d=>[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?b3(n,[0,0]):v5,c=a?A.map(d=>[...cA(d,l),d[2]]):A,x=a?lA(o):v5,i=dt(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function v3(e,t,n,o){let r=t.landmarks.length>=r5.count?r5.symmetryLine:_e.symmetryLine,s=0,A=v5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=aA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=dt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Pe.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=b3(-s,c),a=T5(t,i,[o,o]),Pe.dispose(i)}else a=T5(t,n,[o,o]);else 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t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,M5),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,p2),t.centersNormalized=L.div(t.centers,p2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,p2),t.endNormalized=L.mul(t.ends,p2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function k3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=L.image.resizeBilinear(e,[Ce,Ce]),n.div=L.div(n.resized,C.tf127),n.normalized=L.sub(n.div,C.tf05);let o=de==null?void 0:de.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((p,y)=>p.size-y.size);n.concat384=L.concat([d[0],d[2]],2),n.concat512=L.concat([d[1],d[3]],2),n.concat=L.concat([n.concat512,n.concat384],1),n.batch=L.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=L.squeeze(o[0]):n.batch=L.squeeze(o);L.dispose(o),n.boxes=yA(n.batch),n.logits=L.slice(n.batch,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.logits),n.scores=L.squeeze(n.sigmoid),n.nms=await L.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d<r.length;d++){let p=A[r[d]];if(p>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=L.slice(n.boxes,[r[d],0],[1,-1]),y.slice=L.slice(n.batch,[r[d],M3-1],[1,-1]),y.squeeze=L.squeeze(y.slice),y.landmarks=L.reshape(y.squeeze,[M3,-1]);let b=await y.bbox.data(),M={startPoint:[b[0],b[1]],endPoint:[b[2],b[3]],landmarks:await y.landmarks.array(),confidence:p},T=u3(M,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce]),m=ft(T,t.face.scale||xA),h=mt(m);h.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&h.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(h),Object.keys(y).forEach(S=>L.dispose(y[S]))}}return Object.keys(n).forEach(d=>L.dispose(n[d])),s}var xe=V(H());var U0,We=0,fA=2.3,P5=Ae.leftEyeLower0,k5=Ae.rightEyeLower0,h2={leftBounds:[P5[0],P5[P5.length-1]],rightBounds:[k5[0],k5[k5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function j3(e){var t,n;return R.initial&&(U0=null),U0?e.debug&&u("cached model:",U0.modelUrl):U0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=U0!=null&&U0.executor&&((n=U0.inputs)!=null&&n[0].shape)?U0.inputs[0].shape[2]:0,We===-1&&(We=64),U0}function pt(e,t,n,o){for(let r=0;r<s5.length;r++){let{key:s,indices:A}=s5[r],a=Ae[`${n}${s}`];if(!o||o.includes(s))for(let l=0;l<A.length;l++){let c=A[l];e[a[l]]=[t[c][0],t[c][1],(t[c][2]+e[a[l]][2])/2]}}}var mA=e=>{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},E3=(e,t,n,o,r,s=!1)=>{let A=mt(ft(h3([e[n],e[o]]),fA)),a=m2(A),l=xe.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=xe.image.flipLeftRight(l);xe.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},z3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s<b2.numCoordinates;s++){let A=e[s*3],a=e[s*3+1],l=e[s*3+2];r.push([(o?1-A/We:A/We)*n[0]+t.startPoint[0],a/We*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(b2.index)}},S3=(e,t,n)=>{let o=e[Ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[Ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function I3(e,t,n){if(!(U0!=null&&U0.executor))return e;let{box:o,boxSize:r,crop:s}=E3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=E3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=xe.concat([s,l]);xe.dispose(s),xe.dispose(l);let x=U0.execute(c);xe.dispose(c);let i=await x.data();xe.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:p}=z3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:b,iris:M}=z3(y,A,a,!1),T=mA(e);Math.abs(T)<30?(pt(e,d,"left",null),pt(e,b,"right",null)):T<1?pt(e,d,"left",["EyeUpper0","EyeLower0"]):pt(e,b,"right",["EyeUpper0","EyeLower0"]);let m=S3(e,p,"left"),h=S3(e,M,"right");return e.concat(m).concat(h)}async function O3(e,t){var s,A,a,l,c,x,i,f,d,p;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((p=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:p.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,b)=>y+=e[b][2],0)/e2.length;for(let y=0;y<n.irisL.length/2;y++)e.push([n.irisL[2*y+0],n.irisL[2*y+1],o]);let r=t2.reduce((y,b)=>y+=e[b][2],0)/t2.length;for(let y=0;y<n.irisR.length/2;y++)e.push([n.irisR[2*y+0],n.irisR[2*y+1],r]);for(let y=0;y<n.eyeL.length/2;y++)e[e2[y]]=[n.eyeL[2*y+0],n.eyeL[2*y+1],e[e2[y]][2]];for(let y=0;y<n.eyeR.length/2;y++)e[t2[y]]=[n.eyeR[2*y+0],n.eyeR[2*y+1],e[t2[y]][2]];for(let y=0;y<n.lips.length/2;y++)e[W2[y]]=[n.lips[2*y+0],n.lips[2*y+1],e[W2[y]][2]];return e}var ge={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},r0=null,D2=0;async function L3(e,t){var l,c,x,i,f,d,p,y,b,M;let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>g()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await k3(e,t),ge.timestamp=g(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=D2;for(let T=0;T<ge.boxes.length;T++){let m=ge.boxes[T],h=0,S,P={id:A++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([h,S,P.tensor]=v3((x=t.face.detector)==null?void 0:x.rotation,m,e,(i=t.face.mesh)!=null&&i.enabled?D2:u2()),t.filter.equalization){let N=P.tensor?await q2(P.tensor):void 0;De.dispose(P.tensor),N&&(P.tensor=N)}if(P.boxScore=Math.round(100*m.confidence)/100,!((f=t.face.mesh)!=null&&f.enabled)||!(r0!=null&&r0.executor)){P.box=xt(m,e),P.boxRaw=yt(m,e),P.score=P.boxScore,P.size=m.size,P.mesh=m.landmarks.map(N=>[(m.startPoint[0]+m.endPoint[0])/2+N[0]*e.shape[2]/u2(),(m.startPoint[1]+m.endPoint[1])/2+N[1]*e.shape[1]/u2()]),P.meshRaw=P.mesh.map(N=>[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&u("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),e0=await N.find(G=>G.shape[G.shape.length-1]===1).data();if(P.faceScore=Math.round(100*e0[0])/100,P.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1)){if(m.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=xt(m,e),P.boxRaw=yt(m,e),P.score=P.boxScore,P.mesh=m.landmarks.map(G=>[(m.startPoint[0]+m.endPoint[0])/2+(m.endPoint[0]+m.startPoint[0])*G[0]/u2(),(m.startPoint[1]+m.endPoint[1])/2+(m.endPoint[1]+m.startPoint[1])*G[1]/u2()]),P.meshRaw=P.mesh.map(G=>[G[0]/(e.shape[2]||1),G[1]/(e.shape[1]||1),(G[2]||0)/a]);for(let G of Object.keys(_e))P.annotations[G]=[P.mesh[_e[G]]]}}else{let G=N.find(t0=>t0.shape[t0.shape.length-1]===1404),_=De.reshape(G,[-1,3]),A0=await _.array();De.dispose(_),(y=t.face.attention)!=null&&y.enabled?A0=await O3(A0,N):(b=t.face.iris)!=null&&b.enabled&&(A0=await I3(A0,P.tensor,D2)),P.mesh=T3(A0,m,h,S,D2),P.meshRaw=P.mesh.map(t0=>[t0[0]/(e.shape[2]||0),t0[1]/(e.shape[1]||0),(t0[2]||0)/a]);for(let t0 of Object.keys(Ae))P.annotations[t0]=Ae[t0].map(z0=>P.mesh[z0]);P.score=P.faceScore;let n0={...R3(P.mesh,m),confidence:m.confidence,landmarks:m.landmarks,size:m.size};P.box=xt(n0,e),P.boxRaw=yt(n0,e),s.push(n0)}De.dispose(N)}P.score>(((M=t.face.detector)==null?void 0:M.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function C3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&u("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await O(e.face.attention.modelPath):r0=await O((r=e.face.mesh)==null?void 0:r.modelPath),D2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var W3=$e,D3=C2;var Y0=V(H());var z5=[],M0,ut=[],F3=0,B3=0,E5=Number.MAX_SAFE_INTEGER,S5=!1;async function H3(e){var t,n,o;return R.initial&&(M0=null),M0?e.debug&&u("cached model:",M0.modelUrl):(M0=await O((t=e.face.emotion)==null?void 0:t.modelPath),S5=((o=(n=M0==null?void 0:M0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,S5?z5=["angry","disgust","fear","happy","neutral","sad","surprise"]:z5=["angry","disgust","fear","happy","sad","surprise","neutral"]),M0}async function j5(e,t,n,o){var A,a;if(!M0)return[];let r=E5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-B3;return t.skipAllowed&&s&&r&&F3===o&&ut[n]&&ut[n].length>0?(E5++,ut[n]):(E5=0,new Promise(async l=>{var x;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let i={},f=M0!=null&&M0.inputs[0].shape?M0.inputs[0].shape[2]:0;if(t.face.emotion.crop>0){let p=t.face.emotion.crop,y=[[p,p,1-p,1-p]];i.resize=Y0.image.cropAndResize(e,y,[0],[f,f])}else i.resize=Y0.image.resizeBilinear(e,[f,f],!1);S5?(i.mul=Y0.mul(i.resize,255),i.normalize=Y0.sub(i.mul,[103.939,116.779,123.68]),i.emotion=M0==null?void 0:M0.execute(i.normalize)):(i.channels=Y0.mul(i.resize,C.rgb),i.grayscale=Y0.sum(i.channels,3,!0),i.grayscaleSub=Y0.sub(i.grayscale,C.tf05),i.grayscaleMul=Y0.mul(i.grayscaleSub,C.tf2),i.emotion=M0==null?void 0:M0.execute(i.grayscaleMul)),B3=g();let d=await i.emotion.data();for(let p=0;p<d.length;p++)d[p]>(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*d[p])/100),emotion:z5[p]});c.sort((p,y)=>y.score-p.score),Object.keys(i).forEach(p=>Y0.dispose(i[p]))}ut[n]=c,F3=o,l(c)}))}var ye=V(H());var j0,Fe=[],V3=0,Z3=0,I5=Number.MAX_SAFE_INTEGER;async function X3(e){var t;return R.initial&&(j0=null),j0?e.debug&&u("cached model:",j0.modelUrl):j0=await O((t=e.face.description)==null?void 0:t.modelPath),j0}function uA(e){let t=e.image||e.tensor||e;if(!(j0!=null&&j0.inputs[0].shape))return t;let n=ye.image.resizeBilinear(t,[j0.inputs[0].shape[2],j0.inputs[0].shape[1]],!1),o=ye.mul(n,C.tf255);return ye.dispose(n),o}async function N5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(j0!=null&&j0.executor))return r;let s=I5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>g()-V3;return t.skipAllowed&&s&&A&&Z3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(I5++,Fe[n]):(I5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=uA(e),p=j0==null?void 0:j0.execute(d);V3=g(),ye.dispose(d);let b=await p.find(Z=>Z.shape[1]===1).data(),M=Math.trunc(200*Math.abs(b[0]-.5))/100;M>(t.face.description.minConfidence||0)&&(r.gender=b[0]<=.5?"female":"male",r.genderScore=Math.min(.99,M));let T=ye.argMax(p.find(Z=>Z.shape[1]===100),1),m=(await T.data())[0];ye.dispose(T);let S=await p.find(Z=>Z.shape[1]===100).data();r.age=Math.round(S[m-1]>S[m+1]?10*m-100*S[m-1]:10*m+100*S[m+1])/10,(Number.isNaN(b[0])||Number.isNaN(S[0]))&&u("faceres error:",{model:j0,result:p});let P=p.find(Z=>Z.shape[1]===1024),N=P?await P.data():[];r.descriptor=Array.from(N),p.forEach(Z=>ye.dispose(Z))}Fe[n]=r,Z3=o,i(r)}))}var g2=.1,O5=.5;function hA(e,t,n){let o=!1,r=n.length-1;for(let s=0;s<n.length;r=s++)n[s].y>t!=n[r].y>t&&e<(n[r].x-n[s].x)*(t-n[s].y)/(n[r].y-n[s].y)+n[s].x&&(o=!o);return o}async function U3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),r=[];for(let A of Ae.silhouette)r.push({x:(e.mesh[A][0]-e.box[0])/e.box[2],y:(e.mesh[A][1]-e.box[1])/e.box[3]});g2&&g2>0&&(r=r.map(A=>({x:A.x>.5?A.x+g2:A.x-g2,y:A.y>.5?A.y+g2:A.y-g2})));for(let A=0;A<t;A++)for(let a=0;a<n;a++)hA(A/t,a/t,r)||(o.set(O5*o.get(0,a,A,0),0,a,A,0),o.set(O5*o.get(0,a,A,1),0,a,A,1),o.set(O5*o.get(0,a,A,2),0,a,A,2));return o.toTensor()}var bt=V(H());var P0,ht=[],L5=Number.MAX_SAFE_INTEGER,Y3=0,K3=0;async function J3(e){var t;return R.initial&&(P0=null),P0?e.debug&&u("cached model:",P0.modelUrl):P0=await O((t=e.face.antispoof)==null?void 0:t.modelPath),P0}async function C5(e,t,n,o){var A,a;if(!(P0!=null&&P0.executor))return 0;let r=(((A=t.face.antispoof)==null?void 0:A.skipTime)||0)>g()-K3,s=L5<(((a=t.face.antispoof)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&Y3===o&&ht[n]?(L5++,ht[n]):(L5=0,new Promise(async l=>{let 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i={age:0,gender:"unknown",genderScore:0,race:[]};(M=t.face.gear)!=null&&M.enabled&&([c.age,c.gender,c.race]=ae.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let m=0;m<d.length;m++)d[m]>(((T=t.face.gear)==null?void 0:T.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[m])/100,race:gA[m]});i.race.sort((m,h)=>h.score-m.score);let y=Array.from(await c.age.data()).map((m,h)=>[TA[h],m]).sort((m,h)=>h[1]-m[1]),b=y[0][0];for(let m=1;m<y.length;m++)b+=y[m][1]*(y[m][0]-b);i.age=Math.round(10*b)/10,Object.keys(c).forEach(m=>vt.dispose(c[m])),F5[n]=i,nn=o,on=g(),l(i)}))}var T2=V(H());var K0,Rt=[],An=0,an=0,G5=Number.MAX_SAFE_INTEGER;async function ln(e){return R.initial&&(K0=null),K0?e.debug&&u("cached model:",K0.modelUrl):K0=await O(e.face.ssrnet.modelPathAge),K0}async function V5(e,t,n,o){var A,a,l,c;if(!K0)return{age:0};let 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A,a,l,c;if(!ie)return{gender:"unknown",genderScore:0};let r=Z5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>g()-xn;return t.skipAllowed&&r&&s&&dn===o&&((l=Mt[n])!=null&&l.gender)&&((c=Mt[n])==null?void 0:c.genderScore)>0?(Z5++,Mt[n]):(Z5=0,new Promise(async x=>{var p;if(!(ie!=null&&ie.inputs[0].shape))return;let i={};i.resize=I0.image.resizeBilinear(e,[ie.inputs[0].shape[2],ie.inputs[0].shape[1]],!1),i.enhance=I0.tidy(()=>{let[y,b,M]=I0.split(i.resize,3,3),T=I0.mul(y,X5[0]),m=I0.mul(b,X5[1]),h=I0.mul(M,X5[2]),S=I0.addN([T,m,h]);return I0.mul(I0.sub(S,C.tf05),2)});let f={gender:"unknown",genderScore:0};(p=t.face.ssrnet)!=null&&p.enabled&&(i.gender=ie.execute(i.enhance));let d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(y=>I0.dispose(i[y])),Mt[n]=f,dn=o,xn=g(),x(f)}))}var Pt=V(H());var J0,U5=[],mn=0,pn=0,un=Number.MAX_SAFE_INTEGER;async function hn(e){var t;return R.initial&&(J0=null),J0?e.debug&&u("cached model:",J0.modelUrl):J0=await O((t=e.face.mobilefacenet)==null?void 0:t.modelPath),J0}async function Y5(e,t,n,o){var A,a;if(!(J0!=null&&J0.executor))return[];let r=un<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>g()-pn;return t.skipAllowed&&s&&r&&mn===o&&U5[n]?(un++,U5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(J0!=null&&J0.inputs[0].shape)){let i={};i.crop=Pt.image.resizeBilinear(e,[J0.inputs[0].shape[2],J0.inputs[0].shape[1]],!1),i.data=J0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>Pt.dispose(i[d]))}U5[n]=c,mn=o,pn=g(),l(c)})}var kt=V(H());var Q0,K5=[],gn=0,Tn=0,vn=Number.MAX_SAFE_INTEGER;async function Rn(e){return R.initial&&(Q0=null),Q0?e.debug&&u("cached model:",Q0.modelUrl):Q0=await 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N5(d[w].tensor||l0.tensor([]),e.config,w,d.length),e.performance.description=R.perfadd?(e.performance.description||0)+Math.trunc(g()-n):Math.trunc(g()-n)),e.analyze("End Description:"),e.config.async&&([o,s,A,a,l,i,r,c,x]=await Promise.all([o,s,A,a,l,i,r,c,x])),e.analyze("Finish Face:"),(z0=e.config.face.ssrnet)!=null&&z0.enabled&&o&&s&&(i={...i,age:o.age,gender:s.gender,genderScore:s.genderScore}),(v=e.config.face.gear)!=null&&v.enabled&&r&&(i={...i,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),(T0=e.config.face.mobilefacenet)!=null&&T0.enabled&&a&&(i.descriptor=a),(L0=e.config.face.insightface)!=null&&L0.enabled&&l&&(i.descriptor=l);let X=(x0=e.config.face.iris)!=null&&x0.enabled?kn(d[w],t.shape[2]):0,X0=(Y=e.config.face.detector)!=null&&Y.return?l0.squeeze(d[w].tensor):null;l0.dispose(d[w].tensor),d[w].tensor&&delete d[w].tensor;let y0={...d[w],id:w};i.age&&(y0.age=i.age),i.gender&&(y0.gender=i.gender),i.genderScore&&(y0.genderScore=i.genderScore),i.descriptor&&(y0.embedding=i.descriptor),i.race&&(y0.race=i.race),A&&(y0.emotion=A),c&&(y0.real=c),x&&(y0.live=x),X>0&&(y0.distance=X),B&&(y0.rotation=B),X0&&(y0.tensor=X0),f.push(y0),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),f};var B0={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=>B0.nameMapping[e],getPoints:e=>B0.pointsMapping[e]},He={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>He.nameMapping[e]},c0={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=>c0.nameMapping[e]},Be=class{constructor(t){k(this,"name");k(this,"curls");k(this,"directions");k(this,"weights");k(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,n,o){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,o])}direction(t,n,o){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,o])}weight(t,n){this.weights[t]=n;let o=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/o)}matchAgainst(t,n){let o=0;for(let r in t){let s=t[r],A=this.curls[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}for(let r in n){let s=n[r],A=this.directions[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}return o/10}};var{thumb:fe,index:ke,middle:we,ring:s2,pinky:A2}=B0,{none:me,half:MA,full:pe}=He,{verticalUp:v2,verticalDown:Ki,horizontalLeft:_5,horizontalRight:PA,diagonalUpRight:kA,diagonalUpLeft:R2,diagonalDownRight:Ji,diagonalDownLeft:Qi}=c0,Ge=new Be("thumbs 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t},Cn=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Wn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A<e.length;A++){if(!((o=(n=e[A].annotations)==null?void 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Et(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function F2(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Gn(e,t,n){let o=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r]];return Hn.image.cropAndResize(t,s,[0],n)}function Vn(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function zt(e,t=1.5){let n=F2(e),o=Et(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function St(e){let t=F2(e),n=Et(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function IA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Zn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return IA(n)}var Fn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}function NA(e,t){let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n}function Bn(e,t){let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let s=0;s<o;s++)n[r].push(Xe(e[r],NA(t,s)))}return n}function e1(e,t){let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=Fn(t[0],t[1]),A=Bn(s,r),a=Fn(-t[0],-t[1]);return Bn(A,a)}function Xn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-Xe(t[0],n),-Xe(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]}function t1(e,t){return[Xe(e,t[0]),Xe(e,t[1])]}var 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),p={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Vn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var _0=V(H());var CA=5,Yn=1.65,Kn=[0,5,9,13,17,1,2],WA=0,DA=2,Jn=0,It=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>t1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return zt(St(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=zt(St(n),Yn);o.palmLandmarks=[];for(let r=0;r<Kn.length;r++)o.palmLandmarks.push(t[Kn[r]].slice(0,2));return o}transformRawCoords(t,n,o,r){let s=Et(n),A=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],a=t.map(d=>[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=e1(o,[0,0]),c=a.map(d=>[...t1(d,l),d[2]]),x=Xn(r),i=[...F2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Jn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(c)if(n.hand.landmarks){let x=n.hand.rotation?Zn(c.palmLandmarks[WA],c.palmLandmarks[DA]):0,i=F2(c),f=[i[0]/t.shape[2],i[1]/t.shape[1]],d=n.hand.rotation&&R.kernels.includes("rotatewithoffset")?_0.image.rotateWithOffset(t,x,0,f):t.clone(),p=e1(-x,i),y=o?this.getBoxForPalmLandmarks(c.palmLandmarks,p):c,b=Gn(y,d,[this.inputSize,this.inputSize]),M=_0.div(b,C.tf255);_0.dispose(b),_0.dispose(d);let[T,m]=this.handPoseModel.execute(M);Jn=g(),_0.dispose(M);let h=(await T.data())[0];if(_0.dispose(T),h>=n.hand.minConfidence/4){let S=_0.reshape(m,[-1,3]),P=await S.array();_0.dispose(m),_0.dispose(S);let N=this.transformRawCoords(P,y,x,p),Z=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...Z,confidence:h};let e0={landmarks:N,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:Z.startPoint,bottomRight:Z.endPoint}};a.push(e0)}else this.storedBoxes[l]=null;_0.dispose(m)}else{let x=zt(St(c),Yn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Qn={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]},i2,l2,_n;async function n1(e,t){let n=await _n.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let x of Object.keys(Qn))s[x]=Qn[x].map(i=>n[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]<a[0]&&(a[0]=x[0]),x[1]<a[1]&&(a[1]=x[1]),x[0]>a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=wt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var n,o;R.initial&&(i2=null,l2=null),!i2||!l2?[i2,l2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",i2.modelUrl),e.debug&&u("cached model:",l2.modelUrl));let t=i2?new jt(i2):void 0;return t&&l2&&(_n=new It(t,l2)),[i2,l2]}var J=V(H());var p0=[null,null],BA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qe=[[0,0],[0,0]],HA=["hand","fist","pinch","point","face","tip","pinchtip"],to=4,no=1.6,GA=512,VA=1.4,Nt=Number.MAX_SAFE_INTEGER,o1=0,Ee=[0,0],m0={boxes:[],hands:[]},oo={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 ro(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&u("cached model:",p0[0].modelUrl);else{_2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function so(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&u("cached model:",p0[1].modelUrl);else{p0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function ZA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,GA),A=Math.round(s*r/8)*8;o.resize=J.image.resizeBilinear(e,[s,A]),o.cast=J.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,BA),o.boxes=J.squeeze(o.rawBoxes,[0,2]),o.scores=J.squeeze(o.rawScores,[0]);let a=J.unstack(o.scores,1);J.dispose(a[to]),a.splice(to,1),o.filtered=J.stack(a,1),J.dispose(a),o.max=J.max(o.filtered,1),o.argmax=J.argMax(o.filtered,1);let l=0;o.nms=await J.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=J.slice(o.boxes,f,1),p=await d.data();J.dispose(d);let y=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=at(y,VA),M=[Math.trunc(y[0]*Ee[0]),Math.trunc(y[1]*Ee[1]),Math.trunc(y[2]*Ee[0]),Math.trunc(y[3]*Ee[1])],T=x[f],m=HA[i[f]],h={id:l++,score:T,box:M,boxRaw:b,label:m};n.push(h)}return Object.keys(o).forEach(f=>J.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={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&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=J.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=wt(o.keypoints);for(let i of Object.keys(oo))o.annotations[i]=oo[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>J.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],Nt++;let n=(t.hand.skipTime||0)>g()-o1,o=Nt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-o1,l=Nt<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):(m0.boxes=await ZA(e,t),o1=g(),m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))),Nt=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x<m0.hands.length;x++){let i=n3(m0.hands[x].keypoints,Ee);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=at(i.box,no),d=at(i.boxRaw,no);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x<m0.hands.length;x++){let i=Me(m0.hands[x].keypoints,Ee);m0.hands[x].box=i.box,m0.hands[x].boxRaw=i.boxRaw}A(m0.hands)})}var Te=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var B2={};Se(B2,{connected:()=>Lt,horizontal:()=>A1,kpt:()=>Ot,relative:()=>i1,vertical:()=>a1});var Ot=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],a1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],i1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Lt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var E=Te(),l1=0;function ao(e,t){var A,a,l,c,x,i,f,d,p,y,b,M,T,m,h,S,P,N,Z,e0,G,_,A0,n0,t0,z0;let n=g();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(E.canvas=e.canvas),e.error&&(E.error=e.error),!E.body||e.body.length!==E.body.length)E.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let T0=e.body[v].box.map((B,X)=>((r-1)*E.body[v].box[X]+B)/r),L0=e.body[v].boxRaw.map((B,X)=>((r-1)*E.body[v].boxRaw[X]+B)/r),x0=e.body[v].keypoints.map((B,X)=>{var X0,y0,ze,w2,d2,E1,z1,S1,j1;return{score:B.score,part:B.part,position:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[0]||0)+(B.position[0]||0))/r:B.position[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[1]||0)+(B.position[1]||0))/r:B.position[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[E.body[v].keypoints[X]?((r-1)*(((X0=E.body[v].keypoints[X].distance)==null?void 0:X0[0])||0)+(((y0=B.distance)==null?void 0:y0[0])||0))/r:(ze=B.distance)==null?void 0:ze[0],E.body[v].keypoints[X]?((r-1)*(((w2=E.body[v].keypoints[X].distance)==null?void 0:w2[1])||0)+(((d2=B.distance)==null?void 0:d2[1])||0))/r:(E1=B.distance)==null?void 0:E1[1],E.body[v].keypoints[X]?((r-1)*(((z1=E.body[v].keypoints[X].distance)==null?void 0:z1[2])||0)+(((S1=B.distance)==null?void 0:S1[2])||0))/r:(j1=B.distance)==null?void 0:j1[2]]}}),Y={},w={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?w=ct:(a=t.body.modelPath)!=null&&a.includes("blazepose")?w=st:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(w=B2);for(let[B,X]of Object.entries(w.connected)){let X0=[];for(let y0=0;y0<X.length-1;y0++){let ze=x0.find(d2=>d2.part===X[y0]),w2=x0.find(d2=>d2.part===X[y0+1]);ze&&w2&&X0.push([ze.position,w2.position])}Y[B]=X0}E.body[v]={...e.body[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.hand||e.hand.length!==E.hand.length)E.hand=JSON.parse(JSON.stringify(e.hand));else for(let v=0;v<e.hand.length;v++){let T0=e.hand[v].box.map((w,B)=>((r-1)*E.hand[v].box[B]+w)/r),L0=e.hand[v].boxRaw.map((w,B)=>((r-1)*E.hand[v].boxRaw[B]+w)/r);E.hand[v].keypoints.length!==e.hand[v].keypoints.length&&(E.hand[v].keypoints=e.hand[v].keypoints);let x0=e.hand[v].keypoints&&e.hand[v].keypoints.length>0?e.hand[v].keypoints.map((w,B)=>w.map((X,X0)=>((r-1)*(E.hand[v].keypoints[B][X0]||1)+(X||0))/r)):[],Y={};if(Object.keys(E.hand[v].annotations).length!==Object.keys(e.hand[v].annotations).length)E.hand[v].annotations=e.hand[v].annotations,Y=E.hand[v].annotations;else if(e.hand[v].annotations)for(let w of Object.keys(e.hand[v].annotations))Y[w]=(i=(x=(c=e.hand[v])==null?void 0:c.annotations)==null?void 0:x[w])!=null&&i[0]?e.hand[v].annotations[w].map((B,X)=>B.map((X0,y0)=>((r-1)*E.hand[v].annotations[w][X][y0]+X0)/r)):null;E.hand[v]={...e.hand[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.face||e.face.length!==E.face.length)E.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let T0=e.face[v].box.map((Y,w)=>((r-1)*E.face[v].box[w]+Y)/r),L0=e.face[v].boxRaw.map((Y,w)=>((r-1)*E.face[v].boxRaw[w]+Y)/r),x0=e.face[v].annotations;if(Object.keys(E.face[v].annotations).length!==Object.keys(e.face[v].annotations).length)E.face[v].annotations=e.face[v].annotations,x0=E.face[v].annotations;else if(e.face[v].annotations)for(let Y of Object.keys(e.face[v].annotations))x0[Y]=(p=(d=(f=e.face[v])==null?void 0:f.annotations)==null?void 0:d[Y])!=null&&p[0]?e.face[v].annotations[Y].map((w,B)=>w.map((X,X0)=>((r-1)*E.face[v].annotations[Y][B][X0]+X)/r)):null;if(e.face[v].rotation){let Y={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Y.matrix=(y=e.face[v].rotation)==null?void 0:y.matrix,Y.angle={roll:((r-1)*(((M=(b=E.face[v].rotation)==null?void 0:b.angle)==null?void 0:M.roll)||0)+(((m=(T=e.face[v].rotation)==null?void 0:T.angle)==null?void 0:m.roll)||0))/r,yaw:((r-1)*(((S=(h=E.face[v].rotation)==null?void 0:h.angle)==null?void 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s=e[0][r],A=Math.round(100*s[51+4])/100;if(A>t.body.minConfidence){let a=[];for(let i=0;i<17;i++){let f=s[3*i+2];if(f>t.body.minConfidence){let d=[s[3*i+1],s[3*i+0]];a.push({part:Ot[i],score:Math.round(100*f)/100,positionRaw:d,position:[Math.round((n.shape[2]||0)*d[0]),Math.round((n.shape[1]||0)*d[1])]})}}let l=Me(a.map(i=>i.position),[n.shape[2],n.shape[1]]),c={};for(let[i,f]of Object.entries(Lt)){let d=[];for(let p=0;p<f.length-1;p++){let y=a.find(M=>M.part===f[p]),b=a.find(M=>M.part===f[p+1]);y&&b&&y.score>(t.body.minConfidence||0)&&b.score>(t.body.minConfidence||0)&&d.push([y.position,b.position])}c[i]=d}let x={id:r,score:A,box:l.box,boxRaw:l.boxRaw,keypoints:[...a],annotations:c};x1(x),o.push(x)}}return o.sort((r,s)=>s.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function f1(e,t){var r;if(!(b0!=null&&b0.executor)||!((r=b0==null?void 0:b0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(c2.boxes.length=0),y1++;let n=(t.body.skipTime||0)>g()-c2.last,o=y1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?c2.bodies:new Promise(async s=>{let A={};y1=0,A.input=fo(e,Wt),A.res=b0==null?void 0:b0.execute(A.input),c2.last=g();let a=await A.res.array();c2.bodies=A.res.shape[2]===17?KA(a,t,e):JA(a,t,e);for(let l of c2.bodies)mo(l,[e.shape[2]||1,e.shape[1]||1]),yo(l.keypoints);Object.keys(A).forEach(l=>po.dispose(A[l])),s(c2.bodies)})}var E0=V(H());var le,Dt=[],bo=0,m1=Number.MAX_SAFE_INTEGER,Bt=0,Ft=2.5;async function go(e){if(!le||R.initial){le=await O(e.object.modelPath);let t=le!=null&&le.executor?Object.values(le.modelSignature.inputs):void 0;Bt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&u("cached model:",le.modelUrl);return le}async function QA(e,t,n){var c,x;let o=0,r=[],s=Bt;for(let i of[1,2,4]){let f=i*13,d=E0.squeeze(e.find(m=>m.shape[1]===f**2&&(m.shape[2]||0)===f2.length)),p=await 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G2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_A=G2.length,H2=G2.reduce((e,t,n)=>(e[t]=n,e),{}),$A=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],V7=$A.map(([e,t])=>[H2[e],H2[t]]),vo=[["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 Ro(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,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 Mo(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,x)=>({id:x,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:f,position:d})=>({score:i,part:f,position:[Math.trunc(d.x*A),Math.trunc(d.y*s)],positionRaw:[d.x/o,d.y/o]})),annotations:{}});return e.map((c,x)=>a(c,x))}var Ht=class{constructor(t,n){k(this,"priorityQueue");k(this,"numberOfElements");k(this,"getElementValue");this.priorityQueue=new 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?P3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?J3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?en(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?X3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)!=null&&A.enabled)&&!this.models.emotion?H3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)!=null&&a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?j3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)!=null&&c.enabled)&&!this.models.facemesh?C3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)!=null&&x.enabled)&&!this.models.gear?rn(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)!=null&&i.enabled)&&!this.models.ssrnetage?ln(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((f=this.instance.config.face.ssrnet)!=null&&f.enabled)&&!this.models.ssrnetgender?yn(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)!=null&&d.enabled)&&!this.models.mobilefacenet?hn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((p=this.instance.config.face.insightface)!=null&&p.enabled)&&!this.models.insightface?Rn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((y=this.instance.config.body.modelPath)!=null&&y.includes("blazepose"))?a3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?A3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((b=this.instance.config.body.modelPath)!=null&&b.includes("efficientpose"))?y3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((M=this.instance.config.body.modelPath)!=null&&M.includes("movenet"))?uo(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("posenet"))?Eo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((h=(m=this.instance.config.hand.detector)==null?void 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a5=R.perfadd?a5+Math.round(g()-o):Math.round(g()-o),t.performance.draw=a5,s}function i5(){f0.faceLabels=Oe.face,f0.bodyLabels=Oe.body,f0.bodyPartLabels=Oe.bodyPart,f0.handLabels=Oe.hand,f0.fingerLabels=Oe.finger,f0.objectLabels=Oe.object,f0.gestureLabels=Oe.gesture}var V0=G(H());var rt={};Se(rt,{connected:()=>c5,kpt:()=>l5});var l5=["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"],c5={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 D=G(H());var re,n2=224,_1,Js=5,st=[8,16,32,32,32];function Qs(){let e=[],t=0;for(;t<Js;){let n=0,o=t;for(;o<st.length&&st[o]===st[t];)n+=2,o++;let r=st[t],s=Math.ceil(n2/r),A=Math.ceil(n2/r);for(let a=0;a<s;++a)for(let l=0;l<A;++l)for(let c=0;c<n;++c)e.push({x:(l+.5)/A,y:(a+.5)/s});t=o}_1={x:D.tensor1d(e.map(n=>n.x)),y:D.tensor1d(e.map(n=>n.y))}}async function $1(e){if(R.initial&&(re=null),!re&&e.body.detector&&e.body.detector.modelPath){re=await N(e.body.detector.modelPath);let t=re!=null&&re.executor?Object.values(re.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&re&&u("cached model:",re.modelUrl);return Qs(),re}var Q1=[5,5];function _s(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),Q1[0]),A=D.mul(D.div(A,n2),Q1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function $s(e,t,n,o){var c,x;let r=[],s={};s.boxes=_s(e,_1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let f=a[i],d=l[i],m=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],y={score:f,boxRaw:d,box:m};r.push(y)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function e3(e,t,n){let o={};o.res=re==null?void 0:re.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await $s(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Me(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function t3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function At(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var G0,x5=256,d5=Number.MAX_SAFE_INTEGER,eA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},it=[],Le=[[0,0],[0,0],[0,0],[0,0]],n3=0,o3=e=>1-1/(1+Math.exp(e)),s3=e=>$1(e);async function A3(e){if(R.initial&&(G0=null),G0)e.debug&&u("cached model:",G0.modelUrl);else{G0=await N(e.body.modelPath);let t=G0!=null&&G0.executor?Object.values(G0.modelSignature.inputs):void 0;x5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return G0}function r3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=V0.image.cropAndResize(e,[n],[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],l=[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];Le=[[0,0],a,l,[0,0]],o.pad=V0.pad(o.cropped||e,Le),o.resize=V0.image.resizeBilinear(o.pad,[t,t]),r=V0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=V0.image.resizeBilinear(o.cropped||e,[t,t]),r=V0.div(o.resize,C.tf255)):r=V0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>V0.dispose(o[a])),r}function tA(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+Le[2][0]+Le[2][1])/t[0]-Le[2][0]),Math.trunc(o.position[1]*(t[1]+Le[1][0]+Le[1][1])/t[1]-Le[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(n){let o=n[2]-n[0],r=n[3]-n[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+n[1],s.positionRaw[1]/o+n[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function nA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),s=e.find(a=>a.part==="rightWrist"),A=e.find(a=>a.part==="rightIndex");r.position[2]=((s.position[2]||0)+(A.position[2]||0))/2}async function oA(e,t,n){if(!(G0!=null&&G0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=G0==null?void 0:G0.execute(e,eA.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>V0.dispose(o[m]));let a=[],l=5;for(let m=0;m<s.length/l;m++){let y=o3(s[l*m+3]),b=o3(s[l*m+4]),M=Math.trunc(100*y*b*r)/100,T=[s[l*m+0]/x5,s[l*m+1]/x5,s[l*m+2]+0],p=[Math.trunc(n[0]*T[0]),Math.trunc(n[1]*T[1]),T[2]],h=[A[l*m+0],A[l*m+1],A[l*m+2]+0];a.push({part:l5[m],positionRaw:T,position:p,distance:h,score:M})}if(r<(t.body.minConfidence||0))return null;nA(a);let c=tA(a,n),x=c.map(m=>m.position),i=Me(x,[n[0],n[1]]),f={};for(let[m,y]of Object.entries(c5)){let b=[];for(let M=0;M<y.length-1;M++){let T=c.find(h=>h.part===y[M]),p=c.find(h=>h.part===y[M+1]);T&&p&&b.push([T.position,p.position])}f[m]=b}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:f}}async function y5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>g()-n3,r=d5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&it!==null)d5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=r3(e,224);l=await e3(c,t,n),V0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;c<l.length;c++){let x=r3(e,256,(a=l[c])==null?void 0:a.boxRaw);it.length=0;let i=await oA(x,t,n);V0.dispose(x),i&&(i.id=c,it.push(i))}n3=g(),d5=0}return it}var D0=G(H());var f2=[{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 Z0,o2=0,f5=[],i3=0,m5=Number.MAX_SAFE_INTEGER;async function l3(e){if(R.initial&&(Z0=null),Z0)e.debug&&u("cached model:",Z0.modelUrl);else{Z0=await N(e.object.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;o2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return Z0}async function rA(e,t,n){if(!e)return[];let o={},r=[],s=await e.array();o.squeeze=D0.squeeze(e);let A=D0.split(o.squeeze,6,1);o.stack=D0.stack([A[1],A[0],A[3],A[2]],1),o.boxes=D0.squeeze(o.stack),o.scores=D0.squeeze(A[4]),o.classes=D0.squeeze(A[5]),D0.dispose([e,...A]),o.nms=await D0.image.nonMaxSuppressionAsync(o.boxes,o.scores,n.object.maxDetected||0,n.object.iouThreshold,n.object.minConfidence||0);let a=await o.nms.data(),l=0;for(let c of Array.from(a)){let x=Math.trunc(100*s[0][c][4])/100,i=s[0][c][5];if(Number.isNaN(i))continue;let f=f2[i].label,[d,m]=[s[0][c][0]/o2,s[0][c][1]/o2],y=[d,m,s[0][c][2]/o2-d,s[0][c][3]/o2-m],b=[Math.trunc(y[0]*t[0]),Math.trunc(y[1]*t[1]),Math.trunc(y[2]*t[0]),Math.trunc(y[3]*t[1])];r.push({id:l++,score:x,class:i,label:f,box:b,boxRaw:y})}return Object.keys(o).forEach(c=>D0.dispose(o[c])),r}async function p5(e,t){if(!(Z0!=null&&Z0.executor))return[];let n=(t.object.skipTime||0)>g()-i3,o=m5<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&f5.length>0?(m5++,f5):(m5=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=D0.image.resizeBilinear(e,[o2,o2]),a=t.object.enabled?Z0==null?void 0:Z0.execute(A,["tower_0/detections"]):null;i3=g(),D0.dispose(A);let l=await rA(a,s,t);f5=l,r(l)}))}var K=G(H());var lt={};Se(lt,{connected:()=>h5,kpt:()=>u5});var u5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],h5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var i0,d3=0,F0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},b5=Number.MAX_SAFE_INTEGER;async function x3(e){return R.initial&&(i0=null),i0?e.debug&&u("cached model:",i0.modelUrl):i0=await N(e.body.modelPath),i0}async function sA(e,t){let[n,o]=e.shape,r=K.reshape(e,[o*n]),s=K.max(r,0),A=(await s.data())[0];if(A>t){let a=K.argMax(r,0),l=K.mod(a,n),c=(await l.data())[0],x=K.div(a,n),i=(await x.data())[0];return K.dispose([r,s,a,l,x]),[c,i,A]}return K.dispose([r,s]),[0,0,A]}async function g5(e,t){if(!(i0!=null&&i0.executor)||!(i0!=null&&i0.inputs[0].shape))return[];let n=(t.body.skipTime||0)>g()-d3,o=b5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(F0.keypoints).length>0?(b5++,[F0]):(b5=0,new Promise(async r=>{let s=K.tidy(()=>{var m,y;let i=K.image.resizeBilinear(e,[((m=i0==null?void 0:i0.inputs[0].shape)==null?void 0:m[2])||0,((y=i0==null?void 0:i0.inputs[0].shape)==null?void 0:y[1])||0],!1),f=K.mul(i,C.tf2);return K.sub(f,C.tf1)}),A;if(t.body.enabled&&(A=i0==null?void 0:i0.execute(s)),d3=g(),K.dispose(s),A){F0.keypoints.length=0;let i=K.squeeze(A);K.dispose(A);let f=K.unstack(i,2);K.dispose(i);for(let d=0;d<f.length;d++){let[m,y,b]=await sA(f[d],t.body.minConfidence);b>(t.body.minConfidence||0)&&F0.keypoints.push({score:Math.round(100*b)/100,part:u5[d],positionRaw:[m/i0.inputs[0].shape[2],y/i0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/i0.inputs[0].shape[2]),Math.round(e.shape[1]*y/i0.inputs[0].shape[1])]})}f.forEach(d=>K.dispose(d))}F0.score=F0.keypoints.reduce((i,f)=>f.score>i?f.score:i,0);let a=F0.keypoints.map(i=>i.position[0]),l=F0.keypoints.map(i=>i.position[1]);F0.box=[Math.min(...a),Math.min(...l),Math.max(...a)-Math.min(...a),Math.max(...l)-Math.min(...l)];let c=F0.keypoints.map(i=>i.positionRaw[0]),x=F0.keypoints.map(i=>i.positionRaw[1]);F0.boxRaw=[Math.min(...c),Math.min(...x),Math.max(...c)-Math.min(...c),Math.max(...x)-Math.min(...x)];for(let[i,f]of Object.entries(h5)){let d=[];for(let m=0;m<f.length-1;m++){let y=F0.keypoints.find(M=>M.part===f[m]),b=F0.keypoints.find(M=>M.part===f[m+1]);y&&b&&y.score>(t.body.minConfidence||0)&&b.score>(t.body.minConfidence||0)&&d.push([y.position,b.position])}F0.annotations[i]=d}r([F0])}))}var l0=G(H());var De=G(H());var O=G(H());var Pe=G(H());var m2=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],ct=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],dt=(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],xt=(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],p3=(e,t,n)=>{let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.landmarks.map(A=>[(A[0]+n[0])*t[0],(A[1]+n[1])*t[1]]);return{startPoint:o,endPoint:r,landmarks:s,confidence:e.confidence}},T5=(e,t,n)=>{let o=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r],A=Pe.image.cropAndResize(t,[s],[0],n),a=Pe.div(A,C.tf255);return Pe.dispose(A),a},yt=(e,t)=>{let n=ct(e),o=m2(e),r=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:o}},ft=e=>{let t=ct(e),n=m2(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(n[0]),Math.round(n[1])]}},u3=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},v5=[[1,0,0],[0,1,0],[0,0,1]],AA=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),aA=(e,t)=>AA(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var f3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],r2=(e,t)=>{let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n},iA=(e,t)=>{let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n},m3=(e,t)=>{let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let s=0;s<o;s++)n[r].push(r2(e[r],iA(t,s)))}return n},h3=(e,t)=>{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=f3(t[0],t[1]),A=m3(s,r),a=f3(-t[0],-t[1]);return m3(A,a)},lA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-r2(t[0],n),-r2(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},cA=(e,t)=>[r2(e,t[0]),r2(e,t[1])];function b3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let r=t.strides[o],s=Math.floor((e+r-1)/r),A=Math.floor((e+r-1)/r),a=t.anchors[o];for(let l=0;l<s;l++){let c=r*(l+.5);for(let x=0;x<A;x++){let i=r*(x+.5);for(let f=0;f<a;f++)n.push([i,c])}}}return n}function g3(e,t,n,o,r){let s=m2(t),A=e.map(d=>[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?h3(n,[0,0]):v5,c=a?A.map(d=>[...cA(d,l),d[2]]):A,x=a?lA(o):v5,i=ct(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function T3(e,t,n,o){let r=t.landmarks.length>=r5.count?r5.symmetryLine:_e.symmetryLine,s=0,A=v5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=aA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=ct(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Pe.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=h3(-s,c),a=T5(t,i,[o,o]),Pe.dispose(i)}else a=T5(t,n,[o,o]);else a=T5(t,n,[o,o]);return[s,A,a]}var dA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},v3=(e,t)=>{let n=dA(e),o=m2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var R3=6,xA=1.4,de,mt=null,Ce=0,p2=null,M3=()=>Ce;async function P3(e){var t;return R.initial&&(de=null),de?e.debug&&u("cached model:",de.modelUrl):de=await N((t=e.face.detector)==null?void 0:t.modelPath),Ce=de.executor&&de.inputs[0].shape?de.inputs[0].shape[2]:256,p2=O.scalar(Ce,"int32"),mt=O.tensor2d(b3(Ce)),de}function yA(e){if(!mt||!p2)return O.zeros([0,0]);let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,mt),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,p2),t.centersNormalized=O.div(t.centers,p2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,p2),t.endNormalized=O.mul(t.ends,p2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function k3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=O.image.resizeBilinear(e,[Ce,Ce]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf05);let o=de==null?void 0:de.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=O.concat([d[0],d[2]],2),n.concat512=O.concat([d[1],d[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=O.squeeze(o[0]):n.batch=O.squeeze(o);O.dispose(o),n.boxes=yA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d<r.length;d++){let m=A[r[d]];if(m>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=O.slice(n.boxes,[r[d],0],[1,-1]),y.slice=O.slice(n.batch,[r[d],R3-1],[1,-1]),y.squeeze=O.squeeze(y.slice),y.landmarks=O.reshape(y.squeeze,[R3,-1]);let b=await y.bbox.data(),M={startPoint:[b[0],b[1]],endPoint:[b[2],b[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=O.slice(mt,[r[d],0],[1,2]);let T=await y.anchor.data(),p=p3(M,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],T),h=yt(p,t.face.scale||xA),S=ft(h);S.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&S.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(S),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var xe=G(H());var U0,We=0,fA=2.3,M5=Ae.leftEyeLower0,P5=Ae.rightEyeLower0,u2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},h2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function j3(e){var t,n;return R.initial&&(U0=null),U0?e.debug&&u("cached model:",U0.modelUrl):U0=await N((t=e.face.iris)==null?void 0:t.modelPath),We=U0!=null&&U0.executor&&((n=U0.inputs)!=null&&n[0].shape)?U0.inputs[0].shape[2]:0,We===-1&&(We=64),U0}function pt(e,t,n,o){for(let r=0;r<s5.length;r++){let{key:s,indices:A}=s5[r],a=Ae[`${n}${s}`];if(!o||o.includes(s))for(let l=0;l<A.length;l++){let c=A[l];e[a[l]]=[t[c][0],t[c][1],(t[c][2]+e[a[l]][2])/2]}}}var mA=e=>{let t=e[u2.leftBounds[0]][2],n=e[u2.rightBounds[0]][2];return t-n},E3=(e,t,n,o,r,s=!1)=>{let A=ft(yt(u3([e[n],e[o]]),fA)),a=m2(A),l=xe.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=xe.image.flipLeftRight(l);xe.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},z3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s<h2.numCoordinates;s++){let A=e[s*3],a=e[s*3+1],l=e[s*3+2];r.push([(o?1-A/We:A/We)*n[0]+t.startPoint[0],a/We*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(h2.index)}},S3=(e,t,n)=>{let o=e[Ae[`${n}EyeUpper0`][h2.upperCenter]][2],r=e[Ae[`${n}EyeLower0`][h2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function I3(e,t,n){if(!(U0!=null&&U0.executor))return e;let{box:o,boxSize:r,crop:s}=E3(e,t,u2.leftBounds[0],u2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=E3(e,t,u2.rightBounds[0],u2.rightBounds[1],n,!0),c=xe.concat([s,l]);xe.dispose(s),xe.dispose(l);let x=U0.execute(c);xe.dispose(c);let i=await x.data();xe.dispose(x);let f=i.slice(0,h2.numCoordinates*3),{rawCoords:d,iris:m}=z3(f,o,r,!0),y=i.slice(h2.numCoordinates*3),{rawCoords:b,iris:M}=z3(y,A,a,!1),T=mA(e);Math.abs(T)<30?(pt(e,d,"left",null),pt(e,b,"right",null)):T<1?pt(e,d,"left",["EyeUpper0","EyeLower0"]):pt(e,b,"right",["EyeUpper0","EyeLower0"]);let p=S3(e,m,"left"),h=S3(e,M,"right");return e.concat(p).concat(h)}async function O3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,b)=>y+=e[b][2],0)/e2.length;for(let y=0;y<n.irisL.length/2;y++)e.push([n.irisL[2*y+0],n.irisL[2*y+1],o]);let r=t2.reduce((y,b)=>y+=e[b][2],0)/t2.length;for(let y=0;y<n.irisR.length/2;y++)e.push([n.irisR[2*y+0],n.irisR[2*y+1],r]);for(let y=0;y<n.eyeL.length/2;y++)e[e2[y]]=[n.eyeL[2*y+0],n.eyeL[2*y+1],e[e2[y]][2]];for(let y=0;y<n.eyeR.length/2;y++)e[t2[y]]=[n.eyeR[2*y+0],n.eyeR[2*y+1],e[t2[y]][2]];for(let y=0;y<n.lips.length/2;y++)e[C2[y]]=[n.lips[2*y+0],n.lips[2*y+1],e[C2[y]][2]];return e}var ge={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},r0=null,W2=0;async function L3(e,t){var l,c,x,i,f,d,m,y,b,M;let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>g()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await k3(e,t),ge.timestamp=g(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=W2;for(let T=0;T<ge.boxes.length;T++){let p=ge.boxes[T],h=0,S,P={id:A++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([h,S,P.tensor]=T3((x=t.face.detector)==null?void 0:x.rotation,p,e,(i=t.face.mesh)!=null&&i.enabled?W2:M3()),t.filter.equalization){let L=P.tensor?await X2(P.tensor):void 0;De.dispose(P.tensor),L&&(P.tensor=L)}if(P.boxScore=Math.round(100*p.confidence)/100,!((f=t.face.mesh)!=null&&f.enabled)||!(r0!=null&&r0.executor)){P.box=dt(p,e),P.boxRaw=xt(p,e),P.score=P.boxScore,P.size=p.size,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(L=>[L[0]/(e.shape[2]||0),L[1]/(e.shape[1]||0),(L[2]||0)/a]);for(let L of Object.keys(_e))P.annotations[L]=[P.mesh[_e[L]]]}else if(!r0)t.debug&&u("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let L=r0.execute(P.tensor),e0=await L.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*e0[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=dt(p,e),P.boxRaw=xt(p,e),P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=L.find(t0=>t0.shape[t0.shape.length-1]===1404),_=De.reshape(Z,[-1,3]),A0=await _.array();De.dispose(_),(y=t.face.attention)!=null&&y.enabled?A0=await O3(A0,L):(b=t.face.iris)!=null&&b.enabled&&(A0=await I3(A0,P.tensor,W2)),P.mesh=g3(A0,p,h,S,W2),P.meshRaw=P.mesh.map(t0=>[t0[0]/(e.shape[2]||0),t0[1]/(e.shape[1]||0),(t0[2]||0)/a]);for(let t0 of Object.keys(Ae))P.annotations[t0]=Ae[t0].map(z0=>P.mesh[z0]);P.score=P.faceScore;let n0={...v3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=dt(n0,e),P.boxRaw=xt(n0,e),s.push(n0)}De.dispose(L)}P.score>(((M=t.face.detector)==null?void 0:M.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function C3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&u("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await N(e.face.attention.modelPath):r0=await N((r=e.face.mesh)==null?void 0:r.modelPath),W2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var W3=$e,D3=L2;var Y0=G(H());var E5=[],M0,ut=[],F3=0,B3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function H3(e){var t,n,o;return R.initial&&(M0=null),M0?e.debug&&u("cached model:",M0.modelUrl):(M0=await N((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=M0==null?void 0:M0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),M0}async function S5(e,t,n,o){var A,a;if(!M0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-B3;return t.skipAllowed&&s&&r&&F3===o&&ut[n]&&ut[n].length>0?(w5++,ut[n]):(w5=0,new Promise(async l=>{var x;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let i={},f=M0!=null&&M0.inputs[0].shape?M0.inputs[0].shape[2]:0;if(t.face.emotion.crop>0){let m=t.face.emotion.crop,y=[[m,m,1-m,1-m]];i.resize=Y0.image.cropAndResize(e,y,[0],[f,f])}else i.resize=Y0.image.resizeBilinear(e,[f,f],!1);z5?(i.mul=Y0.mul(i.resize,255),i.normalize=Y0.sub(i.mul,[103.939,116.779,123.68]),i.emotion=M0==null?void 0:M0.execute(i.normalize)):(i.channels=Y0.mul(i.resize,C.rgb),i.grayscale=Y0.sum(i.channels,3,!0),i.grayscaleSub=Y0.sub(i.grayscale,C.tf05),i.grayscaleMul=Y0.mul(i.grayscaleSub,C.tf2),i.emotion=M0==null?void 0:M0.execute(i.grayscaleMul)),B3=g();let d=await i.emotion.data();for(let m=0;m<d.length;m++)d[m]>(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*d[m])/100),emotion:E5[m]});c.sort((m,y)=>y.score-m.score),Object.keys(i).forEach(m=>Y0.dispose(i[m]))}ut[n]=c,F3=o,l(c)}))}var ye=G(H());var j0,Fe=[],V3=0,Z3=0,j5=Number.MAX_SAFE_INTEGER;async function X3(e){var t;return R.initial&&(j0=null),j0?e.debug&&u("cached model:",j0.modelUrl):j0=await N((t=e.face.description)==null?void 0:t.modelPath),j0}function uA(e){let t=e.image||e.tensor||e;if(!(j0!=null&&j0.inputs[0].shape))return t;let 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i={};i.crop=Pt.image.resizeBilinear(e,[J0.inputs[0].shape[2],J0.inputs[0].shape[1]],!1),i.data=J0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>Pt.dispose(i[d]))}q5[n]=c,mn=o,pn=g(),l(c)})}var kt=G(H());var Q0,Y5=[],gn=0,Tn=0,vn=Number.MAX_SAFE_INTEGER;async function Rn(e){return R.initial&&(Q0=null),Q0?e.debug&&u("cached model:",Q0.modelUrl):Q0=await N(e.face.insightface.modelPath),Q0}async function K5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=vn<(((A=t.face.insightface)==null?void 0:A.skipFrames)||0),s=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>g()-Tn;return t.skipAllowed&&s&&r&&gn===o&&Y5[n]?(vn++,Y5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.insightface)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=kt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await 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Et(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function D2(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Gn(e,t,n){let o=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r]];return Hn.image.cropAndResize(t,s,[0],n)}function Vn(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function zt(e,t=1.5){let n=D2(e),o=Et(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function St(e){let t=D2(e),n=Et(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function IA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Zn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return IA(n)}var Fn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}function NA(e,t){let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n}function Bn(e,t){let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let s=0;s<o;s++)n[r].push(Xe(e[r],NA(t,s)))}return n}function $5(e,t){let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=Fn(t[0],t[1]),A=Bn(s,r),a=Fn(-t[0],-t[1]);return Bn(A,a)}function Xn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-Xe(t[0],n),-Xe(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]}function e1(e,t){return[Xe(e,t[0]),Xe(e,t[1])]}var 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),m={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Vn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var _0=G(H());var CA=5,Yn=1.65,Kn=[0,5,9,13,17,1,2],WA=0,DA=2,Jn=0,It=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return zt(St(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=zt(St(n),Yn);o.palmLandmarks=[];for(let r=0;r<Kn.length;r++)o.palmLandmarks.push(t[Kn[r]].slice(0,2));return o}transformRawCoords(t,n,o,r){let s=Et(n),A=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],a=t.map(d=>[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=$5(o,[0,0]),c=a.map(d=>[...e1(d,l),d[2]]),x=Xn(r),i=[...D2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Jn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(c)if(n.hand.landmarks){let x=n.hand.rotation?Zn(c.palmLandmarks[WA],c.palmLandmarks[DA]):0,i=D2(c),f=[i[0]/t.shape[2],i[1]/t.shape[1]],d=n.hand.rotation&&R.kernels.includes("rotatewithoffset")?_0.image.rotateWithOffset(t,x,0,f):t.clone(),m=$5(-x,i),y=o?this.getBoxForPalmLandmarks(c.palmLandmarks,m):c,b=Gn(y,d,[this.inputSize,this.inputSize]),M=_0.div(b,C.tf255);_0.dispose(b),_0.dispose(d);let[T,p]=this.handPoseModel.execute(M);Jn=g(),_0.dispose(M);let h=(await T.data())[0];if(_0.dispose(T),h>=n.hand.minConfidence/4){let S=_0.reshape(p,[-1,3]),P=await S.array();_0.dispose(p),_0.dispose(S);let L=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(L);this.storedBoxes[l]={...V,confidence:h};let e0={landmarks:L,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(e0)}else this.storedBoxes[l]=null;_0.dispose(p)}else{let x=zt(St(c),Yn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Qn={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]},i2,l2,_n;async function t1(e,t){let n=await _n.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let x of Object.keys(Qn))s[x]=Qn[x].map(i=>n[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]<a[0]&&(a[0]=x[0]),x[1]<a[1]&&(a[1]=x[1]),x[0]>a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=wt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var n,o;R.initial&&(i2=null,l2=null),!i2||!l2?[i2,l2]=await Promise.all([e.hand.enabled?N((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?N((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",i2.modelUrl),e.debug&&u("cached model:",l2.modelUrl));let t=i2?new jt(i2):void 0;return t&&l2&&(_n=new It(t,l2)),[i2,l2]}var J=G(H());var p0=[null,null],BA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qe=[[0,0],[0,0]],HA=["hand","fist","pinch","point","face","tip","pinchtip"],to=4,no=1.6,GA=512,VA=1.4,Nt=Number.MAX_SAFE_INTEGER,n1=0,Ee=[0,0],m0={boxes:[],hands:[]},oo={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 ro(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&u("cached model:",p0[0].modelUrl);else{Q2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await N((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function so(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&u("cached model:",p0[1].modelUrl);else{p0[1]=await N((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function ZA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,GA),A=Math.round(s*r/8)*8;o.resize=J.image.resizeBilinear(e,[s,A]),o.cast=J.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,BA),o.boxes=J.squeeze(o.rawBoxes,[0,2]),o.scores=J.squeeze(o.rawScores,[0]);let a=J.unstack(o.scores,1);J.dispose(a[to]),a.splice(to,1),o.filtered=J.stack(a,1),J.dispose(a),o.max=J.max(o.filtered,1),o.argmax=J.argMax(o.filtered,1);let l=0;o.nms=await J.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=J.slice(o.boxes,f,1),m=await d.data();J.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],b=At(y,VA),M=[Math.trunc(y[0]*Ee[0]),Math.trunc(y[1]*Ee[1]),Math.trunc(y[2]*Ee[0]),Math.trunc(y[3]*Ee[1])],T=x[f],p=HA[i[f]],h={id:l++,score:T,box:M,boxRaw:b,label:p};n.push(h)}return Object.keys(o).forEach(f=>J.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function o1(e,t,n){let o={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&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=J.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=wt(o.keypoints);for(let i of Object.keys(oo))o.annotations[i]=oo[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>J.dispose(r[l]))}return o}async function r1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],Nt++;let n=(t.hand.skipTime||0)>g()-n1,o=Nt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-n1,l=Nt<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))):(m0.boxes=await ZA(e,t),n1=g(),m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))),Nt=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x<m0.hands.length;x++){let i=t3(m0.hands[x].keypoints,Ee);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=At(i.box,no),d=At(i.boxRaw,no);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x<m0.hands.length;x++){let i=Me(m0.hands[x].keypoints,Ee);m0.hands[x].box=i.box,m0.hands[x].boxRaw=i.boxRaw}A(m0.hands)})}var Te=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var F2={};Se(F2,{connected:()=>Lt,horizontal:()=>s1,kpt:()=>Ot,relative:()=>a1,vertical:()=>A1});var Ot=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],s1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],A1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],a1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Lt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var E=Te(),i1=0;function ao(e,t){var A,a,l,c,x,i,f,d,m,y,b,M,T,p,h,S,P,L,V,e0,Z,_,A0,n0,t0,z0;let n=g();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(E.canvas=e.canvas),e.error&&(E.error=e.error),!E.body||e.body.length!==E.body.length)E.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let T0=e.body[v].box.map((B,X)=>((r-1)*E.body[v].box[X]+B)/r),L0=e.body[v].boxRaw.map((B,X)=>((r-1)*E.body[v].boxRaw[X]+B)/r),x0=e.body[v].keypoints.map((B,X)=>{var X0,y0,ze,k2,d2,w1,E1,z1,S1;return{score:B.score,part:B.part,position:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[0]||0)+(B.position[0]||0))/r:B.position[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[1]||0)+(B.position[1]||0))/r:B.position[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[E.body[v].keypoints[X]?((r-1)*(((X0=E.body[v].keypoints[X].distance)==null?void 0:X0[0])||0)+(((y0=B.distance)==null?void 0:y0[0])||0))/r:(ze=B.distance)==null?void 0:ze[0],E.body[v].keypoints[X]?((r-1)*(((k2=E.body[v].keypoints[X].distance)==null?void 0:k2[1])||0)+(((d2=B.distance)==null?void 0:d2[1])||0))/r:(w1=B.distance)==null?void 0:w1[1],E.body[v].keypoints[X]?((r-1)*(((E1=E.body[v].keypoints[X].distance)==null?void 0:E1[2])||0)+(((z1=B.distance)==null?void 0:z1[2])||0))/r:(S1=B.distance)==null?void 0:S1[2]]}}),Y={},w={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?w=lt:(a=t.body.modelPath)!=null&&a.includes("blazepose")?w=rt:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(w=F2);for(let[B,X]of Object.entries(w.connected)){let X0=[];for(let y0=0;y0<X.length-1;y0++){let ze=x0.find(d2=>d2.part===X[y0]),k2=x0.find(d2=>d2.part===X[y0+1]);ze&&k2&&X0.push([ze.position,k2.position])}Y[B]=X0}E.body[v]={...e.body[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.hand||e.hand.length!==E.hand.length)E.hand=JSON.parse(JSON.stringify(e.hand));else for(let v=0;v<e.hand.length;v++){let T0=e.hand[v].box.map((w,B)=>((r-1)*E.hand[v].box[B]+w)/r),L0=e.hand[v].boxRaw.map((w,B)=>((r-1)*E.hand[v].boxRaw[B]+w)/r);E.hand[v].keypoints.length!==e.hand[v].keypoints.length&&(E.hand[v].keypoints=e.hand[v].keypoints);let x0=e.hand[v].keypoints&&e.hand[v].keypoints.length>0?e.hand[v].keypoints.map((w,B)=>w.map((X,X0)=>((r-1)*(E.hand[v].keypoints[B][X0]||1)+(X||0))/r)):[],Y={};if(Object.keys(E.hand[v].annotations).length!==Object.keys(e.hand[v].annotations).length)E.hand[v].annotations=e.hand[v].annotations,Y=E.hand[v].annotations;else if(e.hand[v].annotations)for(let w of Object.keys(e.hand[v].annotations))Y[w]=(i=(x=(c=e.hand[v])==null?void 0:c.annotations)==null?void 0:x[w])!=null&&i[0]?e.hand[v].annotations[w].map((B,X)=>B.map((X0,y0)=>((r-1)*E.hand[v].annotations[w][X][y0]+X0)/r)):null;E.hand[v]={...e.hand[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.face||e.face.length!==E.face.length)E.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let T0=e.face[v].box.map((Y,w)=>((r-1)*E.face[v].box[w]+Y)/r),L0=e.face[v].boxRaw.map((Y,w)=>((r-1)*E.face[v].boxRaw[w]+Y)/r),x0=e.face[v].annotations;if(Object.keys(E.face[v].annotations).length!==Object.keys(e.face[v].annotations).length)E.face[v].annotations=e.face[v].annotations,x0=E.face[v].annotations;else if(e.face[v].annotations)for(let Y of Object.keys(e.face[v].annotations))x0[Y]=(m=(d=(f=e.face[v])==null?void 0:f.annotations)==null?void 0:d[Y])!=null&&m[0]?e.face[v].annotations[Y].map((w,B)=>w.map((X,X0)=>((r-1)*E.face[v].annotations[Y][B][X0]+X)/r)):null;if(e.face[v].rotation){let Y={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Y.matrix=(y=e.face[v].rotation)==null?void 0:y.matrix,Y.angle={roll:((r-1)*(((M=(b=E.face[v].rotation)==null?void 0:b.angle)==null?void 0:M.roll)||0)+(((p=(T=e.face[v].rotation)==null?void 0:T.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((S=(h=E.face[v].rotation)==null?void 0:h.angle)==null?void 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n=e.keypoints.findIndex(r=>r.part===t[0]),o=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]<e.keypoints[o].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[o],e.keypoints[o]=r}}for(let t of A1){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),o=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]<e.keypoints[o].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of a1){let o=e.keypoints.findIndex(c=>c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),s=e.keypoints.findIndex(c=>c&&c.part===n[0]),A=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[s]||!e.keypoints[A])continue;let 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e;$0.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]],n.pad=Ue.pad(e,$0.padding),n.resize=Ue.image.resizeBilinear(n.pad,[t,t]);let o=Ue.cast(n.resize,"int32");return Object.keys(n).forEach(A=>Ue.dispose(n[A])),o}function mo(e,t){e.keypoints=e.keypoints.filter(o=>o==null?void 0:o.position);for(let o of e.keypoints)o.position=[o.position[0]*(t[0]+$0.padding[2][0]+$0.padding[2][1])/t[0]-$0.padding[2][0],o.position[1]*(t[1]+$0.padding[1][0]+$0.padding[1][1])/t[1]-$0.padding[1][0]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1]];let n=Me(e.keypoints.map(o=>o.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var b0,Wt=0,x1=Number.MAX_SAFE_INTEGER,c2={boxes:[],bodies:[],last:0};async function uo(e){var t;return R.initial&&(b0=null),b0?e.debug&&u("cached 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s=e[0][r],A=Math.round(100*s[51+4])/100;if(A>t.body.minConfidence){let a=[];for(let i=0;i<17;i++){let f=s[3*i+2];if(f>t.body.minConfidence){let d=[s[3*i+1],s[3*i+0]];a.push({part:Ot[i],score:Math.round(100*f)/100,positionRaw:d,position:[Math.round((n.shape[2]||0)*d[0]),Math.round((n.shape[1]||0)*d[1])]})}}let l=Me(a.map(i=>i.position),[n.shape[2],n.shape[1]]),c={};for(let[i,f]of Object.entries(Lt)){let d=[];for(let m=0;m<f.length-1;m++){let y=a.find(M=>M.part===f[m]),b=a.find(M=>M.part===f[m+1]);y&&b&&y.score>(t.body.minConfidence||0)&&b.score>(t.body.minConfidence||0)&&d.push([y.position,b.position])}c[i]=d}let x={id:r,score:A,box:l.box,boxRaw:l.boxRaw,keypoints:[...a],annotations:c};d1(x),o.push(x)}}return o.sort((r,s)=>s.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function y1(e,t){var r;if(!(b0!=null&&b0.executor)||!((r=b0==null?void 0:b0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(c2.boxes.length=0),x1++;let n=(t.body.skipTime||0)>g()-c2.last,o=x1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?c2.bodies:new Promise(async s=>{let A={};x1=0,A.input=fo(e,Wt),A.res=b0==null?void 0:b0.execute(A.input),c2.last=g();let a=await A.res.array();c2.bodies=A.res.shape[2]===17?KA(a,t,e):JA(a,t,e);for(let l of c2.bodies)mo(l,[e.shape[2]||1,e.shape[1]||1]),yo(l.keypoints);Object.keys(A).forEach(l=>po.dispose(A[l])),s(c2.bodies)})}var E0=G(H());var le,Dt=[],bo=0,f1=Number.MAX_SAFE_INTEGER,Bt=0,Ft=2.5;async function go(e){if(!le||R.initial){le=await N(e.object.modelPath);let t=le!=null&&le.executor?Object.values(le.modelSignature.inputs):void 0;Bt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&u("cached model:",le.modelUrl);return le}async function QA(e,t,n){var c,x;let o=0,r=[],s=Bt;for(let i of[1,2,4]){let f=i*13,d=E0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)===f2.length)),m=await d.array(),y=E0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)<f2.length)),b=E0.reshape(y,[-1,4,(((c=y.shape)==null?void 0:c[1])||0)/4]),M=E0.argMax(b,2),T=await M.array();for(let p=0;p<d.shape[0];p++)for(let h=0;h<(((x=d.shape)==null?void 0:x[1])||0);h++){let S=m[p][h];if(S>(n.object.minConfidence||0)&&h!==61){let P=(.5+Math.trunc(p%f))/f,L=(.5+Math.trunc(p/f))/f,V=T[p].map(v=>v*(f/i/s)),[e0,Z]=[P-Ft/i*V[0],L-Ft/i*V[1]],[_,A0]=[P+Ft/i*V[2]-e0,L+Ft/i*V[3]-Z],n0=[e0,Z,_,A0];n0=n0.map(v=>Math.max(0,Math.min(v,1)));let t0=[n0[0]*t[0],n0[1]*t[1],n0[2]*t[0],n0[3]*t[1]],z0={id:o++,score:Math.round(100*S)/100,class:h+1,label:f2[h].label,box:t0.map(v=>Math.trunc(v)),boxRaw:n0};r.push(z0)}}E0.dispose([d,y,b,M])}let A=r.map(i=>[i.boxRaw[1],i.boxRaw[0],i.boxRaw[3],i.boxRaw[2]]),a=r.map(i=>i.score),l=[];if(A&&A.length>0){let i=await E0.image.nonMaxSuppressionAsync(A,a,n.object.maxDetected||0,n.object.iouThreshold,n.object.minConfidence);l=Array.from(await i.data()),E0.dispose(i)}return r=r.filter((i,f)=>l.includes(f)).sort((i,f)=>f.score-i.score),r}async function m1(e,t){if(!(le!=null&&le.executor))return[];let n=(t.object.skipTime||0)>g()-bo,o=f1<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&Dt.length>0?(f1++,Dt):(f1=0,!R.kernels.includes("mod")||!R.kernels.includes("sparsetodense")?Dt:new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=E0.image.resizeBilinear(e,[Bt,Bt],!1),a=E0.div(A,C.tf255),l=E0.transpose(a,[0,3,1,2]),c;t.object.enabled&&(c=le.execute(l)),bo=g();let x=await QA(c,s,t);Dt=x,E0.dispose([A,a,l,...c]),r(x)}))}var H0=G(H());var H2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_A=H2.length,B2=H2.reduce((e,t,n)=>(e[t]=n,e),{}),$A=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Vi=$A.map(([e,t])=>[B2[e],B2[t]]),vo=[["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 Ro(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,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 Mo(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,x)=>({id:x,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:f,position:d})=>({score:i,part:f,position:[Math.trunc(d.x*A),Math.trunc(d.y*s)],positionRaw:[d.x/o,d.y/o]})),annotations:{}});return e.map((c,x)=>a(c,x))}var Ht=class{constructor(t,n){k(this,"priorityQueue");k(this,"numberOfElements");k(this,"getElementValue");this.priorityQueue=new 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d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function ma(e){let t;return typeof createImageBitmap=="function"?t=await xa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ya(e):t=await fa(e),t}async function pa(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],p=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let b=0;b<d.length;b++)d[b]===-1&&(d[b]=b===0?1:64);let y=d0.zeros(d,p);try{let b=f.execute(y);r.push(i),Array.isArray(b)?b.forEach(M=>d0.dispose(M)):d0.dispose(b)}catch(b){e.config.debug&&u("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&u("tensor leak:",A-o)}async function Wo(e,t){await L2(e,!1);let n=g();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await pa(e);let r=await ma(e),s=g();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var k2,Z2,X2,qt,Ye,w1=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env",R);k(this,"draw",rt);k(this,"match",Ct);k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");ue(this,k2,void 0);ue(this,Z2,void 0);ue(this,X2,void 0);k(this,"analyze",(...t)=>{if(!q0(this,Z2))return;let n=this.tf.engine().state.numTensors,o=q0(this,k2);Re(this,k2,n);let r=n-o;r!==0&&u(...t,r)});ue(this,qt,t=>{if(!q0(this,X2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ce.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"webcam",new Q2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(ce.version.tfjs||ce.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=t5,Object.defineProperty(this,"version",{value:t5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),X1(this.config),this.tf=ce,this.state="idle",Re(this,k2,0),Re(this,Z2,!1),Re(this,X2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new V2(this),i5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=W3,this.faceUVMap=D3,Vt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,$t(),R.initial=!0}validate(t){let n=Jt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return K2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await K2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Io(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await io(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Oo(o.tensor,this.config)),ce.dispose(o.tensor),r}compare(t,n){return Z1(this.config,t,n)}async init(){await L2(this,!0),await this.tf.ready(),$t()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await L2(this,!1)||u("error: backend check failed"),await ce.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return ao(t,this.config)}async warmup(t){let n=g(),o=await Wo(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,M,T,m,h,S,P,N,Z,e0,G,_,A0,n0,t0,z0,v,T0,L0,x0,Y;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=q0(this,qt).call(this,t);s&&(u(s,t),this.emit("error"),o(Te(s)));let A=g();await this.load(),r=g(),this.state="image";let a=await K2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=g(),this.config.skipAllowed=await V1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Q5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await Q5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),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 f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?T1(a.tensor,f):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?y5(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?c=this.config.body.enabled?g5(a.tensor,f):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?f1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=g(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?await T1(a.tensor,f):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?c=this.config.body.enabled?await y5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await g5(a.tensor,f):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(c=this.config.body.enabled?await f1(a.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((e0=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&e0.includes("handdetect")?x=this.config.hand.enabled?n1(a.tensor,d):[]:(_=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&_.includes("handtrack")&&(x=this.config.hand.enabled?s1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(n0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&n0.includes("handdetect")?x=this.config.hand.enabled?await n1(a.tensor,d):[]:(z0=(t0=this.config.hand.detector)==null?void 0:t0.modelPath)!=null&&z0.includes("handtrack")&&(x=this.config.hand.enabled?await s1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((v=this.config.object.modelPath)!=null&&v.includes("nanodet")?i=this.config.object.enabled?p1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?p5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(L0=this.config.object.modelPath)!=null&&L0.includes("nanodet")?i=this.config.object.enabled?await p1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await p5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(r=g(),p=[...Cn(l),...Ln(c),...Dn(x),...Wn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let y=((Y=this.process.tensor)==null?void 0:Y.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:p,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Co(l,c,x,p,y)}},ce.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(q0(this,Ye)[t.id]||(this.config.debug&&u("video start",t.id),q0(this,Ye)[t.id]=!0),!t.paused&&q0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),q0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),q0(this,Ye)[t.id]=!1)}};k2=new WeakMap,Z2=new WeakMap,X2=new WeakMap,qt=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models});
+2Q==`;async function xa(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(A=>A.blob()),n,o;switch(e.config.warmup){case"face":n=await t(Zt);break;case"body":case"full":n=await t(Xt);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function ya(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+Zt;break;case"full":case"body":n="data:image/jpeg;base64,"+Xt;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(R.Image)o=new R.Image;else{t(void 0);return}o.onload=async()=>{let r=se(o.naturalWidth,o.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function fa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Zt):n=t(Xt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function ma(e){let t;return typeof createImageBitmap=="function"?t=await xa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ya(e):t=await fa(e),t}async function pa(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],m=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let b=0;b<d.length;b++)d[b]===-1&&(d[b]=b===0?1:64);let y=d0.zeros(d,m);try{let b=f.execute(y);r.push(i),Array.isArray(b)?b.forEach(M=>d0.dispose(M)):d0.dispose(b)}catch(b){e.config.debug&&u("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&u("tensor leak:",A-o)}async function Wo(e,t){await O2(e,!1);let n=g();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await pa(e);let r=await ma(e),s=g();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,V2,Z2,qt,Ye,k1=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env",R);k(this,"draw",ot);k(this,"match",Ct);k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");ue(this,P2,void 0);ue(this,V2,void 0);ue(this,Z2,void 0);k(this,"analyze",(...t)=>{if(!q0(this,V2))return;let n=this.tf.engine().state.numTensors,o=q0(this,P2);Re(this,P2,n);let r=n-o;r!==0&&u(...t,r)});ue(this,qt,t=>{if(!q0(this,Z2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ce.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"webcam",new J2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(ce.version.tfjs||ce.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=t5,Object.defineProperty(this,"version",{value:t5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),Z1(this.config),this.tf=ce,this.state="idle",Re(this,P2,0),Re(this,V2,!1),Re(this,Z2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new G2(this),i5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=W3,this.faceUVMap=D3,Vt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,$t(),R.initial=!0}validate(t){let n=Jt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return Y2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await Y2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Io(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await io(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Oo(o.tensor,this.config)),ce.dispose(o.tensor),r}compare(t,n){return V1(this.config,t,n)}async init(){await O2(this,!0),await this.tf.ready(),$t()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await O2(this,!1)||u("error: backend check failed"),await ce.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return ao(t,this.config)}async warmup(t){let n=g(),o=await Wo(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,M,T,p,h,S,P,L,V,e0,Z,_,A0,n0,t0,z0,v,T0,L0,x0,Y;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=q0(this,qt).call(this,t);s&&(u(s,t),this.emit("error"),o(Te(s)));let A=g();await this.load(),r=g(),this.state="image";let a=await Y2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=g(),this.config.skipAllowed=await G1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?J5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await J5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),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 f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?g1(a.tensor,f):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?y5(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?c=this.config.body.enabled?g5(a.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?y1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=g(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?await g1(a.tensor,f):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?c=this.config.body.enabled?await y5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await g5(a.tensor,f):[]:(L=this.config.body.modelPath)!=null&&L.includes("movenet")&&(c=this.config.body.enabled?await y1(a.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((e0=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&e0.includes("handdetect")?x=this.config.hand.enabled?t1(a.tensor,d):[]:(_=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&_.includes("handtrack")&&(x=this.config.hand.enabled?r1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(n0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&n0.includes("handdetect")?x=this.config.hand.enabled?await t1(a.tensor,d):[]:(z0=(t0=this.config.hand.detector)==null?void 0:t0.modelPath)!=null&&z0.includes("handtrack")&&(x=this.config.hand.enabled?await r1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((v=this.config.object.modelPath)!=null&&v.includes("nanodet")?i=this.config.object.enabled?m1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?p5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(L0=this.config.object.modelPath)!=null&&L0.includes("nanodet")?i=this.config.object.enabled?await m1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await p5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=g(),m=[...Cn(l),...Ln(c),...Dn(x),...Wn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let y=((Y=this.process.tensor)==null?void 0:Y.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Co(l,c,x,m,y)}},ce.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(q0(this,Ye)[t.id]||(this.config.debug&&u("video start",t.id),q0(this,Ye)[t.id]=!0),!t.paused&&q0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),q0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),q0(this,Ye)[t.id]=!1)}};P2=new WeakMap,V2=new WeakMap,Z2=new WeakMap,qt=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models});
diff --git a/dist/human.node.js b/dist/human.node.js
index 06deb31d..e58a6a94 100644
--- a/dist/human.node.js
+++ b/dist/human.node.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
-"use strict";var Wo=Object.create;var E2=Object.defineProperty;var Do=Object.getOwnPropertyDescriptor;var Fo=Object.getOwnPropertyNames;var Bo=Object.getPrototypeOf,Ho=Object.prototype.hasOwnProperty;var Go=(e,t,n)=>t in e?E2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Vo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var n in t)E2(e,n,{get:t[n],enumerable:!0})},j1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Fo(t))!Ho.call(e,r)&&r!==n&&E2(e,r,{get:()=>t[r],enumerable:!(o=Do(t,r))||o.enumerable});return e};var V=(e,t,n)=>(n=e!=null?Wo(Bo(e)):{},j1(t||!e||!e.__esModule?E2(n,"default",{value:e,enumerable:!0}):n,e)),Zo=e=>j1(E2({},"__esModule",{value:!0}),e);var k=(e,t,n)=>(Go(e,typeof t!="symbol"?t+"":t,n),n),I1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var q0=(e,t,n)=>(I1(e,t,"read from private field"),n?n.call(e):t.get(e)),ue=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},Re=(e,t,n,o)=>(I1(e,t,"write to private field"),o?o.call(e,n):t.set(e,n),n);var H=Vo((ga,Yt)=>{"use strict";var qt=Object.defineProperty,Xo=Object.getOwnPropertyDescriptor,qo=Object.getOwnPropertyNames,Uo=Object.prototype.hasOwnProperty,Yo=(e,t)=>{for(var n in t)qt(e,n,{get:t[n],enumerable:!0})},Xt=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of qo(t))!Uo.call(e,r)&&r!==n&&qt(e,r,{get:()=>t[r],enumerable:!(o=Xo(t,r))||o.enumerable});return e},Ko=(e,t,n)=>(Xt(e,t,"default"),n&&Xt(n,t,"default")),Jo=e=>Xt(qt({},"__esModule",{value:!0}),e),Ut={};Yo(Ut,{version:()=>nr});Yt.exports=Jo(Ut);Ko(Ut,require("@tensorflow/tfjs-node"),Yt.exports);var N1="4.2.0",Qo="4.2.0",_o="4.2.0",$o="4.2.0",er="4.2.0",tr="0.0.1-alpha.17",nr={tfjs:N1,"tfjs-core":N1,"tfjs-converter":Qo,"tfjs-backend-cpu":_o,"tfjs-backend-webgl":$o,"tfjs-backend-wasm":er,"tfjs-backend-webgpu":tr}});var ha={};Se(ha,{Env:()=>z2,Human:()=>k1,default:()=>k1,defaults:()=>Ke,draw:()=>nt,empty:()=>Te,env:()=>R,match:()=>Ot,models:()=>P1});module.exports=Zo(ha);var ce=V(H());function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function O1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Kt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Kt(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function a0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let s=n[r],A=o[r];Array.isArray(s)&&Array.isArray(A)?n[r]=s.concat(...A):t(s)&&t(A)?n[r]=a0(s,A):n[r]=A}),n),{})}var Ke={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var C0=V(H());var I=V(H());var L1=`
+"use strict";var Wo=Object.create;var w2=Object.defineProperty;var Do=Object.getOwnPropertyDescriptor;var Fo=Object.getOwnPropertyNames;var Bo=Object.getPrototypeOf,Ho=Object.prototype.hasOwnProperty;var Go=(e,t,n)=>t in e?w2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Vo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var n in t)w2(e,n,{get:t[n],enumerable:!0})},S1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Fo(t))!Ho.call(e,r)&&r!==n&&w2(e,r,{get:()=>t[r],enumerable:!(o=Do(t,r))||o.enumerable});return e};var G=(e,t,n)=>(n=e!=null?Wo(Bo(e)):{},S1(t||!e||!e.__esModule?w2(n,"default",{value:e,enumerable:!0}):n,e)),Zo=e=>S1(w2({},"__esModule",{value:!0}),e);var k=(e,t,n)=>(Go(e,typeof t!="symbol"?t+"":t,n),n),j1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var q0=(e,t,n)=>(j1(e,t,"read from private field"),n?n.call(e):t.get(e)),ue=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member 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ha={};Se(ha,{Env:()=>E2,Human:()=>P1,default:()=>P1,defaults:()=>Ke,draw:()=>tt,empty:()=>Te,env:()=>R,match:()=>Ot,models:()=>M1});module.exports=Zo(ha);var ce=G(H());function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function N1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Kt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Kt(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function a0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let s=n[r],A=o[r];Array.isArray(s)&&Array.isArray(A)?n[r]=s.concat(...A):t(s)&&t(A)?n[r]=a0(s,A):n[r]=A}),n),{})}var Ke={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var C0=G(H());var I=G(H());var O1=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +14,7 @@
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var C1=`
+`;var L1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +26,7 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,W1=`
+`,C1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +38,7 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,D1=`
+`,W1=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +51,7 @@
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,F1=`
+`,D1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +74,7 @@
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,B1=`
+`,F1=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,8 +96,8 @@
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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W0={cacheModels:!0,cacheSupported:!0,verbose:!0,debug:!1,modelBasePath:""},S0={};async function ds(e,t){return W0.debug&&u("load model fetch:",e,t),fetch(e,t)}function V1(e){W0.cacheModels=e.cacheModels,W0.verbose=e.debug,W0.modelBasePath=e.modelBasePath}async function N(e){var c,x,i,f;let t=N1(W0.modelBasePath,e||"");t.toLowerCase().endsWith(".json")||(t+=".json");let n=t.includes("/")?t.split("/"):t.split("\\"),o=n[n.length-1].replace(".json",""),r="indexeddb://"+o;S0[o]={name:o,sizeFromManifest:0,sizeLoadedWeights:0,sizeDesired:$t[o],inCache:!1,url:""},W0.cacheSupported=typeof indexedDB!="undefined";let s={};try{s=W0.cacheSupported&&W0.cacheModels?await y2.io.listModels():{}}catch(d){W0.cacheSupported=!1}S0[o].inCache=W0.cacheSupported&&W0.cacheModels&&Object.keys(s).includes(r),S0[o].url=S0[o].inCache?r:t;let A=typeof fetch=="undefined"?{}:{fetchFunc:(d,m)=>ds(d,m)},a=new y2.GraphModel(S0[o].url,A),l=!1;try{a.findIOHandler(),W0.debug&&u("model load 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     [gender] [genderScore]%
     age: [age] years
@@ -118,7 +118,7 @@
     live: [live]%
     [emotions]
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t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let r=t.strides[o],s=Math.floor((e+r-1)/r),A=Math.floor((e+r-1)/r),a=t.anchors[o];for(let l=0;l<s;l++){let c=r*(l+.5);for(let x=0;x<A;x++){let i=r*(x+.5);for(let f=0;f<a;f++)n.push([i,c])}}}return n}function g3(e,t,n,o,r){let s=m2(t),A=e.map(d=>[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?h3(n,[0,0]):T5,c=a?A.map(d=>[...cA(d,l),d[2]]):A,x=a?lA(o):T5,i=lt(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function T3(e,t,n,o){let r=t.landmarks.length>=o5.count?o5.symmetryLine:_e.symmetryLine,s=0,A=T5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=aA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=lt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Pe.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=h3(-s,c),a=g5(t,i,[o,o]),Pe.dispose(i)}else a=g5(t,n,[o,o]);else 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t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,R5),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,p2),t.centersNormalized=L.div(t.centers,p2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,p2),t.endNormalized=L.mul(t.ends,p2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function P3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=L.image.resizeBilinear(e,[Ce,Ce]),n.div=L.div(n.resized,C.tf127),n.normalized=L.sub(n.div,C.tf05);let o=de==null?void 0:de.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((p,y)=>p.size-y.size);n.concat384=L.concat([d[0],d[2]],2),n.concat512=L.concat([d[1],d[3]],2),n.concat=L.concat([n.concat512,n.concat384],1),n.batch=L.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=L.squeeze(o[0]):n.batch=L.squeeze(o);L.dispose(o),n.boxes=yA(n.batch),n.logits=L.slice(n.batch,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.logits),n.scores=L.squeeze(n.sigmoid),n.nms=await L.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d<r.length;d++){let p=A[r[d]];if(p>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=L.slice(n.boxes,[r[d],0],[1,-1]),y.slice=L.slice(n.batch,[r[d],R3-1],[1,-1]),y.squeeze=L.squeeze(y.slice),y.landmarks=L.reshape(y.squeeze,[R3,-1]);let b=await y.bbox.data(),M={startPoint:[b[0],b[1]],endPoint:[b[2],b[3]],landmarks:await y.landmarks.array(),confidence:p},T=p3(M,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce]),m=xt(T,t.face.scale||xA),h=yt(m);h.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&h.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(h),Object.keys(y).forEach(S=>L.dispose(y[S]))}}return Object.keys(n).forEach(d=>L.dispose(n[d])),s}var xe=V(H());var U0,We=0,fA=2.3,M5=Ae.leftEyeLower0,P5=Ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function S3(e){var t,n;return R.initial&&(U0=null),U0?e.debug&&u("cached model:",U0.modelUrl):U0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=U0!=null&&U0.executor&&((n=U0.inputs)!=null&&n[0].shape)?U0.inputs[0].shape[2]:0,We===-1&&(We=64),U0}function ft(e,t,n,o){for(let r=0;r<r5.length;r++){let{key:s,indices:A}=r5[r],a=Ae[`${n}${s}`];if(!o||o.includes(s))for(let l=0;l<A.length;l++){let c=A[l];e[a[l]]=[t[c][0],t[c][1],(t[c][2]+e[a[l]][2])/2]}}}var mA=e=>{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},w3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(u3([e[n],e[o]]),fA)),a=m2(A),l=xe.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=xe.image.flipLeftRight(l);xe.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},E3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s<b2.numCoordinates;s++){let A=e[s*3],a=e[s*3+1],l=e[s*3+2];r.push([(o?1-A/We:A/We)*n[0]+t.startPoint[0],a/We*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(b2.index)}},z3=(e,t,n)=>{let o=e[Ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[Ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function j3(e,t,n){if(!(U0!=null&&U0.executor))return e;let{box:o,boxSize:r,crop:s}=w3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=w3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=xe.concat([s,l]);xe.dispose(s),xe.dispose(l);let x=U0.execute(c);xe.dispose(c);let i=await x.data();xe.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:p}=E3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:b,iris:M}=E3(y,A,a,!1),T=mA(e);Math.abs(T)<30?(ft(e,d,"left",null),ft(e,b,"right",null)):T<1?ft(e,d,"left",["EyeUpper0","EyeLower0"]):ft(e,b,"right",["EyeUpper0","EyeLower0"]);let m=z3(e,p,"left"),h=z3(e,M,"right");return e.concat(m).concat(h)}async function N3(e,t){var s,A,a,l,c,x,i,f,d,p;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((p=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:p.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,b)=>y+=e[b][2],0)/e2.length;for(let y=0;y<n.irisL.length/2;y++)e.push([n.irisL[2*y+0],n.irisL[2*y+1],o]);let r=t2.reduce((y,b)=>y+=e[b][2],0)/t2.length;for(let y=0;y<n.irisR.length/2;y++)e.push([n.irisR[2*y+0],n.irisR[2*y+1],r]);for(let y=0;y<n.eyeL.length/2;y++)e[e2[y]]=[n.eyeL[2*y+0],n.eyeL[2*y+1],e[e2[y]][2]];for(let y=0;y<n.eyeR.length/2;y++)e[t2[y]]=[n.eyeR[2*y+0],n.eyeR[2*y+1],e[t2[y]][2]];for(let y=0;y<n.lips.length/2;y++)e[L2[y]]=[n.lips[2*y+0],n.lips[2*y+1],e[L2[y]][2]];return e}var ge={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},r0=null,C2=0;async function O3(e,t){var l,c,x,i,f,d,p,y,b,M;let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>g()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await P3(e,t),ge.timestamp=g(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=C2;for(let T=0;T<ge.boxes.length;T++){let m=ge.boxes[T],h=0,S,P={id:A++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([h,S,P.tensor]=T3((x=t.face.detector)==null?void 0:x.rotation,m,e,(i=t.face.mesh)!=null&&i.enabled?C2:u2()),t.filter.equalization){let N=P.tensor?await Z2(P.tensor):void 0;De.dispose(P.tensor),N&&(P.tensor=N)}if(P.boxScore=Math.round(100*m.confidence)/100,!((f=t.face.mesh)!=null&&f.enabled)||!(r0!=null&&r0.executor)){P.box=ct(m,e),P.boxRaw=dt(m,e),P.score=P.boxScore,P.size=m.size,P.mesh=m.landmarks.map(N=>[(m.startPoint[0]+m.endPoint[0])/2+N[0]*e.shape[2]/u2(),(m.startPoint[1]+m.endPoint[1])/2+N[1]*e.shape[1]/u2()]),P.meshRaw=P.mesh.map(N=>[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&u("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),e0=await N.find(G=>G.shape[G.shape.length-1]===1).data();if(P.faceScore=Math.round(100*e0[0])/100,P.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1)){if(m.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(m,e),P.boxRaw=dt(m,e),P.score=P.boxScore,P.mesh=m.landmarks.map(G=>[(m.startPoint[0]+m.endPoint[0])/2+(m.endPoint[0]+m.startPoint[0])*G[0]/u2(),(m.startPoint[1]+m.endPoint[1])/2+(m.endPoint[1]+m.startPoint[1])*G[1]/u2()]),P.meshRaw=P.mesh.map(G=>[G[0]/(e.shape[2]||1),G[1]/(e.shape[1]||1),(G[2]||0)/a]);for(let G of Object.keys(_e))P.annotations[G]=[P.mesh[_e[G]]]}}else{let G=N.find(t0=>t0.shape[t0.shape.length-1]===1404),_=De.reshape(G,[-1,3]),A0=await _.array();De.dispose(_),(y=t.face.attention)!=null&&y.enabled?A0=await N3(A0,N):(b=t.face.iris)!=null&&b.enabled&&(A0=await j3(A0,P.tensor,C2)),P.mesh=g3(A0,m,h,S,C2),P.meshRaw=P.mesh.map(t0=>[t0[0]/(e.shape[2]||0),t0[1]/(e.shape[1]||0),(t0[2]||0)/a]);for(let t0 of Object.keys(Ae))P.annotations[t0]=Ae[t0].map(z0=>P.mesh[z0]);P.score=P.faceScore;let n0={...v3(P.mesh,m),confidence:m.confidence,landmarks:m.landmarks,size:m.size};P.box=ct(n0,e),P.boxRaw=dt(n0,e),s.push(n0)}De.dispose(N)}P.score>(((M=t.face.detector)==null?void 0:M.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function L3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&u("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await O(e.face.attention.modelPath):r0=await O((r=e.face.mesh)==null?void 0:r.modelPath),C2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var C3=$e,W3=O2;var Y0=V(H());var E5=[],M0,mt=[],D3=0,F3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function B3(e){var t,n,o;return R.initial&&(M0=null),M0?e.debug&&u("cached model:",M0.modelUrl):(M0=await O((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=M0==null?void 0:M0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),M0}async function S5(e,t,n,o){var A,a;if(!M0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-F3;return t.skipAllowed&&s&&r&&D3===o&&mt[n]&&mt[n].length>0?(w5++,mt[n]):(w5=0,new Promise(async l=>{var x;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let i={},f=M0!=null&&M0.inputs[0].shape?M0.inputs[0].shape[2]:0;if(t.face.emotion.crop>0){let p=t.face.emotion.crop,y=[[p,p,1-p,1-p]];i.resize=Y0.image.cropAndResize(e,y,[0],[f,f])}else i.resize=Y0.image.resizeBilinear(e,[f,f],!1);z5?(i.mul=Y0.mul(i.resize,255),i.normalize=Y0.sub(i.mul,[103.939,116.779,123.68]),i.emotion=M0==null?void 0:M0.execute(i.normalize)):(i.channels=Y0.mul(i.resize,C.rgb),i.grayscale=Y0.sum(i.channels,3,!0),i.grayscaleSub=Y0.sub(i.grayscale,C.tf05),i.grayscaleMul=Y0.mul(i.grayscaleSub,C.tf2),i.emotion=M0==null?void 0:M0.execute(i.grayscaleMul)),F3=g();let d=await i.emotion.data();for(let p=0;p<d.length;p++)d[p]>(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*d[p])/100),emotion:E5[p]});c.sort((p,y)=>y.score-p.score),Object.keys(i).forEach(p=>Y0.dispose(i[p]))}mt[n]=c,D3=o,l(c)}))}var ye=V(H());var j0,Fe=[],G3=0,V3=0,j5=Number.MAX_SAFE_INTEGER;async function Z3(e){var t;return R.initial&&(j0=null),j0?e.debug&&u("cached model:",j0.modelUrl):j0=await O((t=e.face.description)==null?void 0:t.modelPath),j0}function uA(e){let t=e.image||e.tensor||e;if(!(j0!=null&&j0.inputs[0].shape))return t;let n=ye.image.resizeBilinear(t,[j0.inputs[0].shape[2],j0.inputs[0].shape[1]],!1),o=ye.mul(n,C.tf255);return ye.dispose(n),o}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(j0!=null&&j0.executor))return r;let s=j5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>g()-G3;return t.skipAllowed&&s&&A&&V3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(j5++,Fe[n]):(j5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=uA(e),p=j0==null?void 0:j0.execute(d);G3=g(),ye.dispose(d);let b=await p.find(Z=>Z.shape[1]===1).data(),M=Math.trunc(200*Math.abs(b[0]-.5))/100;M>(t.face.description.minConfidence||0)&&(r.gender=b[0]<=.5?"female":"male",r.genderScore=Math.min(.99,M));let T=ye.argMax(p.find(Z=>Z.shape[1]===100),1),m=(await T.data())[0];ye.dispose(T);let S=await p.find(Z=>Z.shape[1]===100).data();r.age=Math.round(S[m-1]>S[m+1]?10*m-100*S[m-1]:10*m+100*S[m+1])/10,(Number.isNaN(b[0])||Number.isNaN(S[0]))&&u("faceres error:",{model:j0,result:p});let P=p.find(Z=>Z.shape[1]===1024),N=P?await P.data():[];r.descriptor=Array.from(N),p.forEach(Z=>ye.dispose(Z))}Fe[n]=r,V3=o,i(r)}))}var g2=.1,N5=.5;function hA(e,t,n){let o=!1,r=n.length-1;for(let s=0;s<n.length;r=s++)n[s].y>t!=n[r].y>t&&e<(n[r].x-n[s].x)*(t-n[s].y)/(n[r].y-n[s].y)+n[s].x&&(o=!o);return o}async function q3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),r=[];for(let A of Ae.silhouette)r.push({x:(e.mesh[A][0]-e.box[0])/e.box[2],y:(e.mesh[A][1]-e.box[1])/e.box[3]});g2&&g2>0&&(r=r.map(A=>({x:A.x>.5?A.x+g2:A.x-g2,y:A.y>.5?A.y+g2:A.y-g2})));for(let A=0;A<t;A++)for(let a=0;a<n;a++)hA(A/t,a/t,r)||(o.set(N5*o.get(0,a,A,0),0,a,A,0),o.set(N5*o.get(0,a,A,1),0,a,A,1),o.set(N5*o.get(0,a,A,2),0,a,A,2));return o.toTensor()}var ut=V(H());var P0,pt=[],O5=Number.MAX_SAFE_INTEGER,U3=0,Y3=0;async function K3(e){var t;return R.initial&&(P0=null),P0?e.debug&&u("cached model:",P0.modelUrl):P0=await O((t=e.face.antispoof)==null?void 0:t.modelPath),P0}async function L5(e,t,n,o){var A,a;if(!(P0!=null&&P0.executor))return 0;let r=(((A=t.face.antispoof)==null?void 0:A.skipTime)||0)>g()-Y3,s=O5<(((a=t.face.antispoof)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&U3===o&&pt[n]?(O5++,pt[n]):(O5=0,new Promise(async l=>{let 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i={age:0,gender:"unknown",genderScore:0,race:[]};(M=t.face.gear)!=null&&M.enabled&&([c.age,c.gender,c.race]=ae.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let m=0;m<d.length;m++)d[m]>(((T=t.face.gear)==null?void 0:T.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[m])/100,race:gA[m]});i.race.sort((m,h)=>h.score-m.score);let y=Array.from(await c.age.data()).map((m,h)=>[TA[h],m]).sort((m,h)=>h[1]-m[1]),b=y[0][0];for(let m=1;m<y.length;m++)b+=y[m][1]*(y[m][0]-b);i.age=Math.round(10*b)/10,Object.keys(c).forEach(m=>gt.dispose(c[m])),D5[n]=i,tn=o,nn=g(),l(i)}))}var T2=V(H());var K0,Tt=[],sn=0,An=0,H5=Number.MAX_SAFE_INTEGER;async function an(e){return R.initial&&(K0=null),K0?e.debug&&u("cached model:",K0.modelUrl):K0=await O(e.face.ssrnet.modelPathAge),K0}async function G5(e,t,n,o){var A,a,l,c;if(!K0)return{age:0};let 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A,a,l,c;if(!ie)return{gender:"unknown",genderScore:0};let r=V5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>g()-dn;return t.skipAllowed&&r&&s&&cn===o&&((l=vt[n])!=null&&l.gender)&&((c=vt[n])==null?void 0:c.genderScore)>0?(V5++,vt[n]):(V5=0,new Promise(async x=>{var p;if(!(ie!=null&&ie.inputs[0].shape))return;let i={};i.resize=I0.image.resizeBilinear(e,[ie.inputs[0].shape[2],ie.inputs[0].shape[1]],!1),i.enhance=I0.tidy(()=>{let[y,b,M]=I0.split(i.resize,3,3),T=I0.mul(y,Z5[0]),m=I0.mul(b,Z5[1]),h=I0.mul(M,Z5[2]),S=I0.addN([T,m,h]);return I0.mul(I0.sub(S,C.tf05),2)});let f={gender:"unknown",genderScore:0};(p=t.face.ssrnet)!=null&&p.enabled&&(i.gender=ie.execute(i.enhance));let d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(y=>I0.dispose(i[y])),vt[n]=f,cn=o,dn=g(),x(f)}))}var Rt=V(H());var J0,q5=[],fn=0,mn=0,pn=Number.MAX_SAFE_INTEGER;async function un(e){var t;return R.initial&&(J0=null),J0?e.debug&&u("cached model:",J0.modelUrl):J0=await O((t=e.face.mobilefacenet)==null?void 0:t.modelPath),J0}async function U5(e,t,n,o){var A,a;if(!(J0!=null&&J0.executor))return[];let r=pn<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>g()-mn;return t.skipAllowed&&s&&r&&fn===o&&q5[n]?(pn++,q5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(J0!=null&&J0.inputs[0].shape)){let i={};i.crop=Rt.image.resizeBilinear(e,[J0.inputs[0].shape[2],J0.inputs[0].shape[1]],!1),i.data=J0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>Rt.dispose(i[d]))}q5[n]=c,fn=o,mn=g(),l(c)})}var Mt=V(H());var Q0,Y5=[],bn=0,gn=0,Tn=Number.MAX_SAFE_INTEGER;async function vn(e){return R.initial&&(Q0=null),Q0?e.debug&&u("cached model:",Q0.modelUrl):Q0=await 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I5(d[w].tensor||l0.tensor([]),e.config,w,d.length),e.performance.description=R.perfadd?(e.performance.description||0)+Math.trunc(g()-n):Math.trunc(g()-n)),e.analyze("End Description:"),e.config.async&&([o,s,A,a,l,i,r,c,x]=await Promise.all([o,s,A,a,l,i,r,c,x])),e.analyze("Finish Face:"),(z0=e.config.face.ssrnet)!=null&&z0.enabled&&o&&s&&(i={...i,age:o.age,gender:s.gender,genderScore:s.genderScore}),(v=e.config.face.gear)!=null&&v.enabled&&r&&(i={...i,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),(T0=e.config.face.mobilefacenet)!=null&&T0.enabled&&a&&(i.descriptor=a),(L0=e.config.face.insightface)!=null&&L0.enabled&&l&&(i.descriptor=l);let X=(x0=e.config.face.iris)!=null&&x0.enabled?Pn(d[w],t.shape[2]):0,X0=(Y=e.config.face.detector)!=null&&Y.return?l0.squeeze(d[w].tensor):null;l0.dispose(d[w].tensor),d[w].tensor&&delete d[w].tensor;let y0={...d[w],id:w};i.age&&(y0.age=i.age),i.gender&&(y0.gender=i.gender),i.genderScore&&(y0.genderScore=i.genderScore),i.descriptor&&(y0.embedding=i.descriptor),i.race&&(y0.race=i.race),A&&(y0.emotion=A),c&&(y0.real=c),x&&(y0.live=x),X>0&&(y0.distance=X),B&&(y0.rotation=B),X0&&(y0.tensor=X0),f.push(y0),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),f};var B0={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=>B0.nameMapping[e],getPoints:e=>B0.pointsMapping[e]},He={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>He.nameMapping[e]},c0={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=>c0.nameMapping[e]},Be=class{constructor(t){k(this,"name");k(this,"curls");k(this,"directions");k(this,"weights");k(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,n,o){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,o])}direction(t,n,o){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,o])}weight(t,n){this.weights[t]=n;let o=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/o)}matchAgainst(t,n){let o=0;for(let r in t){let s=t[r],A=this.curls[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}for(let r in n){let s=n[r],A=this.directions[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}return o/10}};var{thumb:fe,index:ke,middle:we,ring:s2,pinky:A2}=B0,{none:me,half:MA,full:pe}=He,{verticalUp:v2,verticalDown:Ki,horizontalLeft:Q5,horizontalRight:PA,diagonalUpRight:kA,diagonalUpLeft:R2,diagonalDownRight:Ji,diagonalDownLeft:Qi}=c0,Ge=new Be("thumbs 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b=Math.sqrt(r*r+a*a),M=Math.sqrt(s*s+l*l),T=Math.sqrt(A*A+c*c),m=Math.max(b,M,T),h=e[0],S=e[1],P=n[0],N=n[1];m===b?(P=n[0],N=n[1]):m===T&&(h=t[0],S=t[1]);let G=zn([h,S],[P,N]),_=En(G,a2.TOTAL_ANGLE_VOTE_POWER);f+=_[0],d+=_[1],p+=_[2];for(let n0 of o){let t0=En(n0,a2.SINGLE_ANGLE_VOTE_POWER);f+=t0[0],d+=t0[1],p+=t0[2]}let A0;return f===Math.max(f,d,p)?A0=jn(l,a,c,i):p===Math.max(d,p)?A0=Sn(s,r,A,x):A0=zA(l,a,c,i,s,r,A,x),A0}function In(e){let t=[],n=[],o=[],r=[];if(!e)return{curls:o,directions:r};for(let s of B0.all){let A=B0.getPoints(s),a=[],l=[];for(let c of A){let x=e[c[0]],i=e[c[1]],f=zn(x,i),d=f[0],p=f[1];a.push(d),l.push(p)}t.push(a),n.push(l)}for(let s of B0.all){let A=s===B0.thumb?1:0,a=B0.getPoints(s),l=e[a[A][0]],c=e[a[A+1][1]],x=e[a[3][1]],i=EA(l,c,x),f=SA(l,c,x,t[s].slice(A));o[s]=i,r[s]=f}return{curls:o,directions:r}}function Pt(e){if(!e||e.length===0)return null;let t=In(e),n={};for(let o of 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t},Ln=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Cn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A<e.length;A++){if(!((o=(n=e[A].annotations)==null?void 0:n.leftEyeIris)!=null&&o[0])||!((s=(r=e[A].annotations)==null?void 0:r.rightEyeIris)!=null&&s[0]))continue;let a=e[A].annotations.leftEyeIris[3][0]-e[A].annotations.leftEyeIris[1][0],l=e[A].annotations.leftEyeIris[4][1]-e[A].annotations.leftEyeIris[2][1],c=Math.abs(a*l),x=e[A].annotations.rightEyeIris[3][0]-e[A].annotations.rightEyeIris[1][0],i=e[A].annotations.rightEyeIris[4][1]-e[A].annotations.rightEyeIris[2][1],f=Math.abs(x*i),d=!1;Math.abs(c-f)/Math.max(c,f)<.25&&(d=!0,t.push({iris:A,gesture:"facing center"}));let y=Math.abs(e[A].mesh[263][0]-e[A].annotations.leftEyeIris[0][0])/e[A].box[2],b=Math.abs(e[A].mesh[33][0]-e[A].annotations.rightEyeIris[0][0])/e[A].box[2];(y>.06||b>.06)&&(d=!1),y>b?y>.05&&t.push({iris:A,gesture:"looking right"}):b>.05&&t.push({iris:A,gesture:"looking left"});let 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kt(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function W2(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Hn(e,t,n){let o=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r]];return Bn.image.cropAndResize(t,s,[0],n)}function Gn(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function wt(e,t=1.5){let n=W2(e),o=kt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function Et(e){let t=W2(e),n=kt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function IA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Vn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return IA(n)}var Dn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}function NA(e,t){let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n}function Fn(e,t){let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let s=0;s<o;s++)n[r].push(Xe(e[r],NA(t,s)))}return n}function $5(e,t){let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=Dn(t[0],t[1]),A=Fn(s,r),a=Dn(-t[0],-t[1]);return Fn(A,a)}function Zn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-Xe(t[0],n),-Xe(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]}function e1(e,t){return[Xe(e,t[0]),Xe(e,t[1])]}var 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),p={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Gn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var _0=V(H());var CA=5,Un=1.65,Yn=[0,5,9,13,17,1,2],WA=0,DA=2,Kn=0,St=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return wt(Et(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=wt(Et(n),Un);o.palmLandmarks=[];for(let r=0;r<Yn.length;r++)o.palmLandmarks.push(t[Yn[r]].slice(0,2));return o}transformRawCoords(t,n,o,r){let s=kt(n),A=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],a=t.map(d=>[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=$5(o,[0,0]),c=a.map(d=>[...e1(d,l),d[2]]),x=Zn(r),i=[...W2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Kn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(c)if(n.hand.landmarks){let x=n.hand.rotation?Vn(c.palmLandmarks[WA],c.palmLandmarks[DA]):0,i=W2(c),f=[i[0]/t.shape[2],i[1]/t.shape[1]],d=n.hand.rotation&&R.kernels.includes("rotatewithoffset")?_0.image.rotateWithOffset(t,x,0,f):t.clone(),p=$5(-x,i),y=o?this.getBoxForPalmLandmarks(c.palmLandmarks,p):c,b=Hn(y,d,[this.inputSize,this.inputSize]),M=_0.div(b,C.tf255);_0.dispose(b),_0.dispose(d);let[T,m]=this.handPoseModel.execute(M);Kn=g(),_0.dispose(M);let h=(await T.data())[0];if(_0.dispose(T),h>=n.hand.minConfidence/4){let S=_0.reshape(m,[-1,3]),P=await S.array();_0.dispose(m),_0.dispose(S);let N=this.transformRawCoords(P,y,x,p),Z=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...Z,confidence:h};let e0={landmarks:N,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:Z.startPoint,bottomRight:Z.endPoint}};a.push(e0)}else this.storedBoxes[l]=null;_0.dispose(m)}else{let x=wt(Et(c),Un),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Jn={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]},i2,l2,Qn;async function t1(e,t){let n=await Qn.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let x of Object.keys(Jn))s[x]=Jn[x].map(i=>n[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]<a[0]&&(a[0]=x[0]),x[1]<a[1]&&(a[1]=x[1]),x[0]>a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Pt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function _n(e){var n,o;R.initial&&(i2=null,l2=null),!i2||!l2?[i2,l2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",i2.modelUrl),e.debug&&u("cached model:",l2.modelUrl));let t=i2?new zt(i2):void 0;return t&&l2&&(Qn=new St(t,l2)),[i2,l2]}var J=V(H());var p0=[null,null],BA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qe=[[0,0],[0,0]],HA=["hand","fist","pinch","point","face","tip","pinchtip"],eo=4,to=1.6,GA=512,VA=1.4,jt=Number.MAX_SAFE_INTEGER,n1=0,Ee=[0,0],m0={boxes:[],hands:[]},no={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 oo(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&u("cached model:",p0[0].modelUrl);else{J2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function ro(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&u("cached model:",p0[1].modelUrl);else{p0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function ZA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,GA),A=Math.round(s*r/8)*8;o.resize=J.image.resizeBilinear(e,[s,A]),o.cast=J.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,BA),o.boxes=J.squeeze(o.rawBoxes,[0,2]),o.scores=J.squeeze(o.rawScores,[0]);let a=J.unstack(o.scores,1);J.dispose(a[eo]),a.splice(eo,1),o.filtered=J.stack(a,1),J.dispose(a),o.max=J.max(o.filtered,1),o.argmax=J.argMax(o.filtered,1);let l=0;o.nms=await J.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=J.slice(o.boxes,f,1),p=await d.data();J.dispose(d);let y=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=st(y,VA),M=[Math.trunc(y[0]*Ee[0]),Math.trunc(y[1]*Ee[1]),Math.trunc(y[2]*Ee[0]),Math.trunc(y[3]*Ee[1])],T=x[f],m=HA[i[f]],h={id:l++,score:T,box:M,boxRaw:b,label:m};n.push(h)}return Object.keys(o).forEach(f=>J.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function o1(e,t,n){let o={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&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=J.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Pt(o.keypoints);for(let i of Object.keys(no))o.annotations[i]=no[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>J.dispose(r[l]))}return o}async function r1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],jt++;let n=(t.hand.skipTime||0)>g()-n1,o=jt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-n1,l=jt<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))):(m0.boxes=await ZA(e,t),n1=g(),m0.hands=await Promise.all(m0.boxes.map(x=>o1(e,x,t))),jt=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x<m0.hands.length;x++){let i=t3(m0.hands[x].keypoints,Ee);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=st(i.box,to),d=st(i.boxRaw,to);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x<m0.hands.length;x++){let i=Me(m0.hands[x].keypoints,Ee);m0.hands[x].box=i.box,m0.hands[x].boxRaw=i.boxRaw}A(m0.hands)})}var Te=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var D2={};Se(D2,{connected:()=>Nt,horizontal:()=>s1,kpt:()=>It,relative:()=>a1,vertical:()=>A1});var It=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],s1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],A1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],a1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Nt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var E=Te(),i1=0;function Ao(e,t){var A,a,l,c,x,i,f,d,p,y,b,M,T,m,h,S,P,N,Z,e0,G,_,A0,n0,t0,z0;let n=g();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(E.canvas=e.canvas),e.error&&(E.error=e.error),!E.body||e.body.length!==E.body.length)E.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let T0=e.body[v].box.map((B,X)=>((r-1)*E.body[v].box[X]+B)/r),L0=e.body[v].boxRaw.map((B,X)=>((r-1)*E.body[v].boxRaw[X]+B)/r),x0=e.body[v].keypoints.map((B,X)=>{var X0,y0,ze,w2,d2,w1,E1,z1,S1;return{score:B.score,part:B.part,position:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[0]||0)+(B.position[0]||0))/r:B.position[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[1]||0)+(B.position[1]||0))/r:B.position[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[E.body[v].keypoints[X]?((r-1)*(((X0=E.body[v].keypoints[X].distance)==null?void 0:X0[0])||0)+(((y0=B.distance)==null?void 0:y0[0])||0))/r:(ze=B.distance)==null?void 0:ze[0],E.body[v].keypoints[X]?((r-1)*(((w2=E.body[v].keypoints[X].distance)==null?void 0:w2[1])||0)+(((d2=B.distance)==null?void 0:d2[1])||0))/r:(w1=B.distance)==null?void 0:w1[1],E.body[v].keypoints[X]?((r-1)*(((E1=E.body[v].keypoints[X].distance)==null?void 0:E1[2])||0)+(((z1=B.distance)==null?void 0:z1[2])||0))/r:(S1=B.distance)==null?void 0:S1[2]]}}),Y={},w={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?w=it:(a=t.body.modelPath)!=null&&a.includes("blazepose")?w=ot:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(w=D2);for(let[B,X]of Object.entries(w.connected)){let X0=[];for(let y0=0;y0<X.length-1;y0++){let ze=x0.find(d2=>d2.part===X[y0]),w2=x0.find(d2=>d2.part===X[y0+1]);ze&&w2&&X0.push([ze.position,w2.position])}Y[B]=X0}E.body[v]={...e.body[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.hand||e.hand.length!==E.hand.length)E.hand=JSON.parse(JSON.stringify(e.hand));else for(let v=0;v<e.hand.length;v++){let T0=e.hand[v].box.map((w,B)=>((r-1)*E.hand[v].box[B]+w)/r),L0=e.hand[v].boxRaw.map((w,B)=>((r-1)*E.hand[v].boxRaw[B]+w)/r);E.hand[v].keypoints.length!==e.hand[v].keypoints.length&&(E.hand[v].keypoints=e.hand[v].keypoints);let x0=e.hand[v].keypoints&&e.hand[v].keypoints.length>0?e.hand[v].keypoints.map((w,B)=>w.map((X,X0)=>((r-1)*(E.hand[v].keypoints[B][X0]||1)+(X||0))/r)):[],Y={};if(Object.keys(E.hand[v].annotations).length!==Object.keys(e.hand[v].annotations).length)E.hand[v].annotations=e.hand[v].annotations,Y=E.hand[v].annotations;else if(e.hand[v].annotations)for(let w of Object.keys(e.hand[v].annotations))Y[w]=(i=(x=(c=e.hand[v])==null?void 0:c.annotations)==null?void 0:x[w])!=null&&i[0]?e.hand[v].annotations[w].map((B,X)=>B.map((X0,y0)=>((r-1)*E.hand[v].annotations[w][X][y0]+X0)/r)):null;E.hand[v]={...e.hand[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.face||e.face.length!==E.face.length)E.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let T0=e.face[v].box.map((Y,w)=>((r-1)*E.face[v].box[w]+Y)/r),L0=e.face[v].boxRaw.map((Y,w)=>((r-1)*E.face[v].boxRaw[w]+Y)/r),x0=e.face[v].annotations;if(Object.keys(E.face[v].annotations).length!==Object.keys(e.face[v].annotations).length)E.face[v].annotations=e.face[v].annotations,x0=E.face[v].annotations;else if(e.face[v].annotations)for(let Y of Object.keys(e.face[v].annotations))x0[Y]=(p=(d=(f=e.face[v])==null?void 0:f.annotations)==null?void 0:d[Y])!=null&&p[0]?e.face[v].annotations[Y].map((w,B)=>w.map((X,X0)=>((r-1)*E.face[v].annotations[Y][B][X0]+X)/r)):null;if(e.face[v].rotation){let Y={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Y.matrix=(y=e.face[v].rotation)==null?void 0:y.matrix,Y.angle={roll:((r-1)*(((M=(b=E.face[v].rotation)==null?void 0:b.angle)==null?void 0:M.roll)||0)+(((m=(T=e.face[v].rotation)==null?void 0:T.angle)==null?void 0:m.roll)||0))/r,yaw:((r-1)*(((S=(h=E.face[v].rotation)==null?void 0:h.angle)==null?void 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n=(t.body.skipTime||0)>g()-c2.last,o=x1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?c2.bodies:new Promise(async s=>{let A={};x1=0,A.input=yo(e,Lt),A.res=b0==null?void 0:b0.execute(A.input),c2.last=g();let a=await A.res.array();c2.bodies=A.res.shape[2]===17?KA(a,t,e):JA(a,t,e);for(let l of c2.bodies)fo(l,[e.shape[2]||1,e.shape[1]||1]),xo(l.keypoints);Object.keys(A).forEach(l=>mo.dispose(A[l])),s(c2.bodies)})}var E0=V(H());var le,Ct=[],ho=0,f1=Number.MAX_SAFE_INTEGER,Dt=0,Wt=2.5;async function bo(e){if(!le||R.initial){le=await O(e.object.modelPath);let t=le!=null&&le.executor?Object.values(le.modelSignature.inputs):void 0;Dt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&u("cached model:",le.modelUrl);return le}async function QA(e,t,n){var c,x;let o=0,r=[],s=Dt;for(let i of[1,2,4]){let f=i*13,d=E0.squeeze(e.find(m=>m.shape[1]===f**2&&(m.shape[2]||0)===f2.length)),p=await 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B2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_A=B2.length,F2=B2.reduce((e,t,n)=>(e[t]=n,e),{}),$A=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],V7=$A.map(([e,t])=>[F2[e],F2[t]]),To=[["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 vo(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,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 Ro(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,x)=>({id:x,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:f,position:d})=>({score:i,part:f,position:[Math.trunc(d.x*A),Math.trunc(d.y*s)],positionRaw:[d.x/o,d.y/o]})),annotations:{}});return e.map((c,x)=>a(c,x))}var Ft=class{constructor(t,n){k(this,"priorityQueue");k(this,"numberOfElements");k(this,"getElementValue");this.priorityQueue=new 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A5=R.perfadd?A5+Math.round(g()-o):Math.round(g()-o),t.performance.draw=A5,s}function a5(){f0.faceLabels=Oe.face,f0.bodyLabels=Oe.body,f0.bodyPartLabels=Oe.bodyPart,f0.handLabels=Oe.hand,f0.fingerLabels=Oe.finger,f0.objectLabels=Oe.object,f0.gestureLabels=Oe.gesture}var V0=G(H());var nt={};Se(nt,{connected:()=>l5,kpt:()=>i5});var i5=["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"],l5={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 D=G(H());var re,n2=224,Q1,Js=5,ot=[8,16,32,32,32];function Qs(){let e=[],t=0;for(;t<Js;){let n=0,o=t;for(;o<ot.length&&ot[o]===ot[t];)n+=2,o++;let r=ot[t],s=Math.ceil(n2/r),A=Math.ceil(n2/r);for(let a=0;a<s;++a)for(let l=0;l<A;++l)for(let c=0;c<n;++c)e.push({x:(l+.5)/A,y:(a+.5)/s});t=o}Q1={x:D.tensor1d(e.map(n=>n.x)),y:D.tensor1d(e.map(n=>n.y))}}async function _1(e){if(R.initial&&(re=null),!re&&e.body.detector&&e.body.detector.modelPath){re=await N(e.body.detector.modelPath);let t=re!=null&&re.executor?Object.values(re.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&re&&u("cached model:",re.modelUrl);return Qs(),re}var J1=[5,5];function _s(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),J1[0]),A=D.mul(D.div(A,n2),J1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function $s(e,t,n,o){var c,x;let r=[],s={};s.boxes=_s(e,Q1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let f=a[i],d=l[i],m=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],y={score:f,boxRaw:d,box:m};r.push(y)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function $1(e,t,n){let o={};o.res=re==null?void 0:re.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await $s(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Me(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function e3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function rt(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var G0,d5=256,c5=Number.MAX_SAFE_INTEGER,eA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},At=[],Le=[[0,0],[0,0],[0,0],[0,0]],t3=0,n3=e=>1-1/(1+Math.exp(e)),r3=e=>_1(e);async function s3(e){if(R.initial&&(G0=null),G0)e.debug&&u("cached model:",G0.modelUrl);else{G0=await N(e.body.modelPath);let t=G0!=null&&G0.executor?Object.values(G0.modelSignature.inputs):void 0;d5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return G0}function o3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=V0.image.cropAndResize(e,[n],[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],l=[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];Le=[[0,0],a,l,[0,0]],o.pad=V0.pad(o.cropped||e,Le),o.resize=V0.image.resizeBilinear(o.pad,[t,t]),r=V0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=V0.image.resizeBilinear(o.cropped||e,[t,t]),r=V0.div(o.resize,C.tf255)):r=V0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>V0.dispose(o[a])),r}function tA(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+Le[2][0]+Le[2][1])/t[0]-Le[2][0]),Math.trunc(o.position[1]*(t[1]+Le[1][0]+Le[1][1])/t[1]-Le[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(n){let o=n[2]-n[0],r=n[3]-n[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+n[1],s.positionRaw[1]/o+n[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function nA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),s=e.find(a=>a.part==="rightWrist"),A=e.find(a=>a.part==="rightIndex");r.position[2]=((s.position[2]||0)+(A.position[2]||0))/2}async function oA(e,t,n){if(!(G0!=null&&G0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=G0==null?void 0:G0.execute(e,eA.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>V0.dispose(o[m]));let a=[],l=5;for(let m=0;m<s.length/l;m++){let y=n3(s[l*m+3]),b=n3(s[l*m+4]),M=Math.trunc(100*y*b*r)/100,T=[s[l*m+0]/d5,s[l*m+1]/d5,s[l*m+2]+0],p=[Math.trunc(n[0]*T[0]),Math.trunc(n[1]*T[1]),T[2]],h=[A[l*m+0],A[l*m+1],A[l*m+2]+0];a.push({part:i5[m],positionRaw:T,position:p,distance:h,score:M})}if(r<(t.body.minConfidence||0))return null;nA(a);let c=tA(a,n),x=c.map(m=>m.position),i=Me(x,[n[0],n[1]]),f={};for(let[m,y]of Object.entries(l5)){let b=[];for(let M=0;M<y.length-1;M++){let T=c.find(h=>h.part===y[M]),p=c.find(h=>h.part===y[M+1]);T&&p&&b.push([T.position,p.position])}f[m]=b}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:f}}async function x5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>g()-t3,r=c5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&At!==null)c5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=o3(e,224);l=await $1(c,t,n),V0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;c<l.length;c++){let x=o3(e,256,(a=l[c])==null?void 0:a.boxRaw);At.length=0;let i=await oA(x,t,n);V0.dispose(x),i&&(i.id=c,At.push(i))}t3=g(),c5=0}return At}var D0=G(H());var f2=[{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 Z0,o2=0,y5=[],a3=0,f5=Number.MAX_SAFE_INTEGER;async function i3(e){if(R.initial&&(Z0=null),Z0)e.debug&&u("cached model:",Z0.modelUrl);else{Z0=await N(e.object.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;o2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return Z0}async function rA(e,t,n){if(!e)return[];let o={},r=[],s=await e.array();o.squeeze=D0.squeeze(e);let A=D0.split(o.squeeze,6,1);o.stack=D0.stack([A[1],A[0],A[3],A[2]],1),o.boxes=D0.squeeze(o.stack),o.scores=D0.squeeze(A[4]),o.classes=D0.squeeze(A[5]),D0.dispose([e,...A]),o.nms=await D0.image.nonMaxSuppressionAsync(o.boxes,o.scores,n.object.maxDetected||0,n.object.iouThreshold,n.object.minConfidence||0);let a=await o.nms.data(),l=0;for(let c of Array.from(a)){let x=Math.trunc(100*s[0][c][4])/100,i=s[0][c][5];if(Number.isNaN(i))continue;let f=f2[i].label,[d,m]=[s[0][c][0]/o2,s[0][c][1]/o2],y=[d,m,s[0][c][2]/o2-d,s[0][c][3]/o2-m],b=[Math.trunc(y[0]*t[0]),Math.trunc(y[1]*t[1]),Math.trunc(y[2]*t[0]),Math.trunc(y[3]*t[1])];r.push({id:l++,score:x,class:i,label:f,box:b,boxRaw:y})}return Object.keys(o).forEach(c=>D0.dispose(o[c])),r}async function m5(e,t){if(!(Z0!=null&&Z0.executor))return[];let n=(t.object.skipTime||0)>g()-a3,o=f5<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&y5.length>0?(f5++,y5):(f5=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=D0.image.resizeBilinear(e,[o2,o2]),a=t.object.enabled?Z0==null?void 0:Z0.execute(A,["tower_0/detections"]):null;a3=g(),D0.dispose(A);let l=await rA(a,s,t);y5=l,r(l)}))}var K=G(H());var at={};Se(at,{connected:()=>u5,kpt:()=>p5});var p5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],u5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var i0,c3=0,F0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},h5=Number.MAX_SAFE_INTEGER;async function d3(e){return R.initial&&(i0=null),i0?e.debug&&u("cached model:",i0.modelUrl):i0=await N(e.body.modelPath),i0}async function sA(e,t){let[n,o]=e.shape,r=K.reshape(e,[o*n]),s=K.max(r,0),A=(await s.data())[0];if(A>t){let a=K.argMax(r,0),l=K.mod(a,n),c=(await l.data())[0],x=K.div(a,n),i=(await x.data())[0];return K.dispose([r,s,a,l,x]),[c,i,A]}return K.dispose([r,s]),[0,0,A]}async function b5(e,t){if(!(i0!=null&&i0.executor)||!(i0!=null&&i0.inputs[0].shape))return[];let n=(t.body.skipTime||0)>g()-c3,o=h5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(F0.keypoints).length>0?(h5++,[F0]):(h5=0,new Promise(async r=>{let s=K.tidy(()=>{var m,y;let i=K.image.resizeBilinear(e,[((m=i0==null?void 0:i0.inputs[0].shape)==null?void 0:m[2])||0,((y=i0==null?void 0:i0.inputs[0].shape)==null?void 0:y[1])||0],!1),f=K.mul(i,C.tf2);return K.sub(f,C.tf1)}),A;if(t.body.enabled&&(A=i0==null?void 0:i0.execute(s)),c3=g(),K.dispose(s),A){F0.keypoints.length=0;let i=K.squeeze(A);K.dispose(A);let f=K.unstack(i,2);K.dispose(i);for(let d=0;d<f.length;d++){let[m,y,b]=await sA(f[d],t.body.minConfidence);b>(t.body.minConfidence||0)&&F0.keypoints.push({score:Math.round(100*b)/100,part:p5[d],positionRaw:[m/i0.inputs[0].shape[2],y/i0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/i0.inputs[0].shape[2]),Math.round(e.shape[1]*y/i0.inputs[0].shape[1])]})}f.forEach(d=>K.dispose(d))}F0.score=F0.keypoints.reduce((i,f)=>f.score>i?f.score:i,0);let a=F0.keypoints.map(i=>i.position[0]),l=F0.keypoints.map(i=>i.position[1]);F0.box=[Math.min(...a),Math.min(...l),Math.max(...a)-Math.min(...a),Math.max(...l)-Math.min(...l)];let c=F0.keypoints.map(i=>i.positionRaw[0]),x=F0.keypoints.map(i=>i.positionRaw[1]);F0.boxRaw=[Math.min(...c),Math.min(...x),Math.max(...c)-Math.min(...c),Math.max(...x)-Math.min(...x)];for(let[i,f]of Object.entries(u5)){let d=[];for(let m=0;m<f.length-1;m++){let y=F0.keypoints.find(M=>M.part===f[m]),b=F0.keypoints.find(M=>M.part===f[m+1]);y&&b&&y.score>(t.body.minConfidence||0)&&b.score>(t.body.minConfidence||0)&&d.push([y.position,b.position])}F0.annotations[i]=d}r([F0])}))}var l0=G(H());var De=G(H());var O=G(H());var Pe=G(H());var m2=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],it=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],lt=(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],ct=(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],m3=(e,t,n)=>{let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.landmarks.map(A=>[(A[0]+n[0])*t[0],(A[1]+n[1])*t[1]]);return{startPoint:o,endPoint:r,landmarks:s,confidence:e.confidence}},g5=(e,t,n)=>{let o=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r],A=Pe.image.cropAndResize(t,[s],[0],n),a=Pe.div(A,C.tf255);return Pe.dispose(A),a},dt=(e,t)=>{let n=it(e),o=m2(e),r=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:o}},xt=e=>{let t=it(e),n=m2(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(n[0]),Math.round(n[1])]}},p3=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},T5=[[1,0,0],[0,1,0],[0,0,1]],AA=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),aA=(e,t)=>AA(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var y3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],r2=(e,t)=>{let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n},iA=(e,t)=>{let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n},f3=(e,t)=>{let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let s=0;s<o;s++)n[r].push(r2(e[r],iA(t,s)))}return n},u3=(e,t)=>{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=y3(t[0],t[1]),A=f3(s,r),a=y3(-t[0],-t[1]);return f3(A,a)},lA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-r2(t[0],n),-r2(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},cA=(e,t)=>[r2(e,t[0]),r2(e,t[1])];function h3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let r=t.strides[o],s=Math.floor((e+r-1)/r),A=Math.floor((e+r-1)/r),a=t.anchors[o];for(let l=0;l<s;l++){let c=r*(l+.5);for(let x=0;x<A;x++){let i=r*(x+.5);for(let f=0;f<a;f++)n.push([i,c])}}}return n}function b3(e,t,n,o,r){let s=m2(t),A=e.map(d=>[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?u3(n,[0,0]):T5,c=a?A.map(d=>[...cA(d,l),d[2]]):A,x=a?lA(o):T5,i=it(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function g3(e,t,n,o){let r=t.landmarks.length>=o5.count?o5.symmetryLine:_e.symmetryLine,s=0,A=T5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=aA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=it(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Pe.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=u3(-s,c),a=g5(t,i,[o,o]),Pe.dispose(i)}else a=g5(t,n,[o,o]);else a=g5(t,n,[o,o]);return[s,A,a]}var dA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},T3=(e,t)=>{let n=dA(e),o=m2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var v3=6,xA=1.4,de,yt=null,Ce=0,p2=null,R3=()=>Ce;async function M3(e){var t;return R.initial&&(de=null),de?e.debug&&u("cached model:",de.modelUrl):de=await N((t=e.face.detector)==null?void 0:t.modelPath),Ce=de.executor&&de.inputs[0].shape?de.inputs[0].shape[2]:256,p2=O.scalar(Ce,"int32"),yt=O.tensor2d(h3(Ce)),de}function yA(e){if(!yt||!p2)return O.zeros([0,0]);let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,yt),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,p2),t.centersNormalized=O.div(t.centers,p2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,p2),t.endNormalized=O.mul(t.ends,p2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function P3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=O.image.resizeBilinear(e,[Ce,Ce]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf05);let o=de==null?void 0:de.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=O.concat([d[0],d[2]],2),n.concat512=O.concat([d[1],d[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=O.squeeze(o[0]):n.batch=O.squeeze(o);O.dispose(o),n.boxes=yA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d<r.length;d++){let m=A[r[d]];if(m>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=O.slice(n.boxes,[r[d],0],[1,-1]),y.slice=O.slice(n.batch,[r[d],v3-1],[1,-1]),y.squeeze=O.squeeze(y.slice),y.landmarks=O.reshape(y.squeeze,[v3,-1]);let b=await y.bbox.data(),M={startPoint:[b[0],b[1]],endPoint:[b[2],b[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=O.slice(yt,[r[d],0],[1,2]);let T=await y.anchor.data(),p=m3(M,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],T),h=dt(p,t.face.scale||xA),S=xt(h);S.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&S.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(S),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var xe=G(H());var U0,We=0,fA=2.3,R5=Ae.leftEyeLower0,M5=Ae.rightEyeLower0,u2={leftBounds:[R5[0],R5[R5.length-1]],rightBounds:[M5[0],M5[M5.length-1]]},h2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function S3(e){var t,n;return R.initial&&(U0=null),U0?e.debug&&u("cached model:",U0.modelUrl):U0=await N((t=e.face.iris)==null?void 0:t.modelPath),We=U0!=null&&U0.executor&&((n=U0.inputs)!=null&&n[0].shape)?U0.inputs[0].shape[2]:0,We===-1&&(We=64),U0}function ft(e,t,n,o){for(let r=0;r<r5.length;r++){let{key:s,indices:A}=r5[r],a=Ae[`${n}${s}`];if(!o||o.includes(s))for(let l=0;l<A.length;l++){let c=A[l];e[a[l]]=[t[c][0],t[c][1],(t[c][2]+e[a[l]][2])/2]}}}var mA=e=>{let t=e[u2.leftBounds[0]][2],n=e[u2.rightBounds[0]][2];return t-n},w3=(e,t,n,o,r,s=!1)=>{let A=xt(dt(p3([e[n],e[o]]),fA)),a=m2(A),l=xe.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=xe.image.flipLeftRight(l);xe.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},E3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s<h2.numCoordinates;s++){let A=e[s*3],a=e[s*3+1],l=e[s*3+2];r.push([(o?1-A/We:A/We)*n[0]+t.startPoint[0],a/We*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(h2.index)}},z3=(e,t,n)=>{let o=e[Ae[`${n}EyeUpper0`][h2.upperCenter]][2],r=e[Ae[`${n}EyeLower0`][h2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function j3(e,t,n){if(!(U0!=null&&U0.executor))return e;let{box:o,boxSize:r,crop:s}=w3(e,t,u2.leftBounds[0],u2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=w3(e,t,u2.rightBounds[0],u2.rightBounds[1],n,!0),c=xe.concat([s,l]);xe.dispose(s),xe.dispose(l);let x=U0.execute(c);xe.dispose(c);let i=await x.data();xe.dispose(x);let f=i.slice(0,h2.numCoordinates*3),{rawCoords:d,iris:m}=E3(f,o,r,!0),y=i.slice(h2.numCoordinates*3),{rawCoords:b,iris:M}=E3(y,A,a,!1),T=mA(e);Math.abs(T)<30?(ft(e,d,"left",null),ft(e,b,"right",null)):T<1?ft(e,d,"left",["EyeUpper0","EyeLower0"]):ft(e,b,"right",["EyeUpper0","EyeLower0"]);let p=z3(e,m,"left"),h=z3(e,M,"right");return e.concat(p).concat(h)}async function N3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,b)=>y+=e[b][2],0)/e2.length;for(let y=0;y<n.irisL.length/2;y++)e.push([n.irisL[2*y+0],n.irisL[2*y+1],o]);let r=t2.reduce((y,b)=>y+=e[b][2],0)/t2.length;for(let y=0;y<n.irisR.length/2;y++)e.push([n.irisR[2*y+0],n.irisR[2*y+1],r]);for(let y=0;y<n.eyeL.length/2;y++)e[e2[y]]=[n.eyeL[2*y+0],n.eyeL[2*y+1],e[e2[y]][2]];for(let y=0;y<n.eyeR.length/2;y++)e[t2[y]]=[n.eyeR[2*y+0],n.eyeR[2*y+1],e[t2[y]][2]];for(let y=0;y<n.lips.length/2;y++)e[O2[y]]=[n.lips[2*y+0],n.lips[2*y+1],e[O2[y]][2]];return e}var ge={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},r0=null,L2=0;async function O3(e,t){var l,c,x,i,f,d,m,y,b,M;let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>g()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await P3(e,t),ge.timestamp=g(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=L2;for(let T=0;T<ge.boxes.length;T++){let p=ge.boxes[T],h=0,S,P={id:A++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([h,S,P.tensor]=g3((x=t.face.detector)==null?void 0:x.rotation,p,e,(i=t.face.mesh)!=null&&i.enabled?L2:R3()),t.filter.equalization){let L=P.tensor?await V2(P.tensor):void 0;De.dispose(P.tensor),L&&(P.tensor=L)}if(P.boxScore=Math.round(100*p.confidence)/100,!((f=t.face.mesh)!=null&&f.enabled)||!(r0!=null&&r0.executor)){P.box=lt(p,e),P.boxRaw=ct(p,e),P.score=P.boxScore,P.size=p.size,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(L=>[L[0]/(e.shape[2]||0),L[1]/(e.shape[1]||0),(L[2]||0)/a]);for(let L of Object.keys(_e))P.annotations[L]=[P.mesh[_e[L]]]}else if(!r0)t.debug&&u("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let L=r0.execute(P.tensor),e0=await L.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*e0[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=lt(p,e),P.boxRaw=ct(p,e),P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=L.find(t0=>t0.shape[t0.shape.length-1]===1404),_=De.reshape(Z,[-1,3]),A0=await _.array();De.dispose(_),(y=t.face.attention)!=null&&y.enabled?A0=await N3(A0,L):(b=t.face.iris)!=null&&b.enabled&&(A0=await j3(A0,P.tensor,L2)),P.mesh=b3(A0,p,h,S,L2),P.meshRaw=P.mesh.map(t0=>[t0[0]/(e.shape[2]||0),t0[1]/(e.shape[1]||0),(t0[2]||0)/a]);for(let t0 of Object.keys(Ae))P.annotations[t0]=Ae[t0].map(z0=>P.mesh[z0]);P.score=P.faceScore;let n0={...T3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=lt(n0,e),P.boxRaw=ct(n0,e),s.push(n0)}De.dispose(L)}P.score>(((M=t.face.detector)==null?void 0:M.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function L3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&u("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await N(e.face.attention.modelPath):r0=await N((r=e.face.mesh)==null?void 0:r.modelPath),L2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var C3=$e,W3=N2;var Y0=G(H());var w5=[],M0,mt=[],D3=0,F3=0,k5=Number.MAX_SAFE_INTEGER,E5=!1;async function B3(e){var t,n,o;return R.initial&&(M0=null),M0?e.debug&&u("cached model:",M0.modelUrl):(M0=await N((t=e.face.emotion)==null?void 0:t.modelPath),E5=((o=(n=M0==null?void 0:M0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,E5?w5=["angry","disgust","fear","happy","neutral","sad","surprise"]:w5=["angry","disgust","fear","happy","sad","surprise","neutral"]),M0}async function z5(e,t,n,o){var A,a;if(!M0)return[];let r=k5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-F3;return t.skipAllowed&&s&&r&&D3===o&&mt[n]&&mt[n].length>0?(k5++,mt[n]):(k5=0,new Promise(async l=>{var x;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let i={},f=M0!=null&&M0.inputs[0].shape?M0.inputs[0].shape[2]:0;if(t.face.emotion.crop>0){let m=t.face.emotion.crop,y=[[m,m,1-m,1-m]];i.resize=Y0.image.cropAndResize(e,y,[0],[f,f])}else 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0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>g()-nn;return t.skipAllowed&&s&&r&&tn===o&&W5[n]?(D5++,W5[n]):(D5=0,new Promise(async l=>{var M,T;if(!(ae!=null&&ae.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];c.resize=gt.image.cropAndResize(e,x,[0],[ae.inputs[0].shape[2],ae.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(M=t.face.gear)!=null&&M.enabled&&([c.age,c.gender,c.race]=ae.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let p=0;p<d.length;p++)d[p]>(((T=t.face.gear)==null?void 0:T.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[p])/100,race:gA[p]});i.race.sort((p,h)=>h.score-p.score);let y=Array.from(await c.age.data()).map((p,h)=>[TA[h],p]).sort((p,h)=>h[1]-p[1]),b=y[0][0];for(let p=1;p<y.length;p++)b+=y[p][1]*(y[p][0]-b);i.age=Math.round(10*b)/10,Object.keys(c).forEach(p=>gt.dispose(c[p])),W5[n]=i,tn=o,nn=g(),l(i)}))}var g2=G(H());var K0,Tt=[],sn=0,An=0,B5=Number.MAX_SAFE_INTEGER;async function an(e){return R.initial&&(K0=null),K0?e.debug&&u("cached model:",K0.modelUrl):K0=await N(e.face.ssrnet.modelPathAge),K0}async function H5(e,t,n,o){var A,a,l,c;if(!K0)return{age:0};let r=B5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>g()-An;return t.skipAllowed&&r&&s&&sn===o&&((l=Tt[n])!=null&&l.age)&&((c=Tt[n])==null?void 0:c.age)>0?(B5++,Tt[n]):(B5=0,new Promise(async x=>{var d;if(!(K0!=null&&K0.inputs)||!K0.inputs[0]||!K0.inputs[0].shape)return;let i={};i.resize=g2.image.resizeBilinear(e,[K0.inputs[0].shape[2],K0.inputs[0].shape[1]],!1),i.enhance=g2.mul(i.resize,C.tf255);let f={age:0};if((d=t.face.ssrnet)!=null&&d.enabled&&(i.age=K0.execute(i.enhance)),i.age){let m=await 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i={};i.crop=Rt.image.resizeBilinear(e,[J0.inputs[0].shape[2],J0.inputs[0].shape[1]],!1),i.data=J0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>Rt.dispose(i[d]))}X5[n]=c,fn=o,mn=g(),l(c)})}var Mt=G(H());var Q0,U5=[],bn=0,gn=0,Tn=Number.MAX_SAFE_INTEGER;async function vn(e){return R.initial&&(Q0=null),Q0?e.debug&&u("cached model:",Q0.modelUrl):Q0=await N(e.face.insightface.modelPath),Q0}async function Y5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=Tn<(((A=t.face.insightface)==null?void 0:A.skipFrames)||0),s=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>g()-gn;return t.skipAllowed&&s&&r&&bn===o&&U5[n]?(Tn++,U5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.insightface)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=Mt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await 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kt(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function C2(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Hn(e,t,n){let o=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r]];return Bn.image.cropAndResize(t,s,[0],n)}function Gn(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function wt(e,t=1.5){let n=C2(e),o=kt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function Et(e){let t=C2(e),n=kt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function IA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Vn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return IA(n)}var Dn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}function NA(e,t){let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n}function Fn(e,t){let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let s=0;s<o;s++)n[r].push(Xe(e[r],NA(t,s)))}return n}function _5(e,t){let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=Dn(t[0],t[1]),A=Fn(s,r),a=Dn(-t[0],-t[1]);return Fn(A,a)}function Zn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-Xe(t[0],n),-Xe(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]}function $5(e,t){return[Xe(e,t[0]),Xe(e,t[1])]}var 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),m={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Gn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var _0=G(H());var CA=5,Un=1.65,Yn=[0,5,9,13,17,1,2],WA=0,DA=2,Kn=0,St=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>$5([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return wt(Et(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=wt(Et(n),Un);o.palmLandmarks=[];for(let r=0;r<Yn.length;r++)o.palmLandmarks.push(t[Yn[r]].slice(0,2));return o}transformRawCoords(t,n,o,r){let s=kt(n),A=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],a=t.map(d=>[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=_5(o,[0,0]),c=a.map(d=>[...$5(d,l),d[2]]),x=Zn(r),i=[...C2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Kn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(c)if(n.hand.landmarks){let x=n.hand.rotation?Vn(c.palmLandmarks[WA],c.palmLandmarks[DA]):0,i=C2(c),f=[i[0]/t.shape[2],i[1]/t.shape[1]],d=n.hand.rotation&&R.kernels.includes("rotatewithoffset")?_0.image.rotateWithOffset(t,x,0,f):t.clone(),m=_5(-x,i),y=o?this.getBoxForPalmLandmarks(c.palmLandmarks,m):c,b=Hn(y,d,[this.inputSize,this.inputSize]),M=_0.div(b,C.tf255);_0.dispose(b),_0.dispose(d);let[T,p]=this.handPoseModel.execute(M);Kn=g(),_0.dispose(M);let h=(await T.data())[0];if(_0.dispose(T),h>=n.hand.minConfidence/4){let S=_0.reshape(p,[-1,3]),P=await S.array();_0.dispose(p),_0.dispose(S);let L=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(L);this.storedBoxes[l]={...V,confidence:h};let e0={landmarks:L,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(e0)}else this.storedBoxes[l]=null;_0.dispose(p)}else{let x=wt(Et(c),Un),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Jn={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]},i2,l2,Qn;async function e1(e,t){let n=await Qn.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let x of Object.keys(Jn))s[x]=Jn[x].map(i=>n[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]<a[0]&&(a[0]=x[0]),x[1]<a[1]&&(a[1]=x[1]),x[0]>a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Pt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function _n(e){var n,o;R.initial&&(i2=null,l2=null),!i2||!l2?[i2,l2]=await Promise.all([e.hand.enabled?N((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?N((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",i2.modelUrl),e.debug&&u("cached model:",l2.modelUrl));let t=i2?new zt(i2):void 0;return t&&l2&&(Qn=new St(t,l2)),[i2,l2]}var J=G(H());var p0=[null,null],BA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qe=[[0,0],[0,0]],HA=["hand","fist","pinch","point","face","tip","pinchtip"],eo=4,to=1.6,GA=512,VA=1.4,jt=Number.MAX_SAFE_INTEGER,t1=0,Ee=[0,0],m0={boxes:[],hands:[]},no={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 oo(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&u("cached model:",p0[0].modelUrl);else{K2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await N((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function ro(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&u("cached model:",p0[1].modelUrl);else{p0[1]=await N((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function ZA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,GA),A=Math.round(s*r/8)*8;o.resize=J.image.resizeBilinear(e,[s,A]),o.cast=J.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,BA),o.boxes=J.squeeze(o.rawBoxes,[0,2]),o.scores=J.squeeze(o.rawScores,[0]);let a=J.unstack(o.scores,1);J.dispose(a[eo]),a.splice(eo,1),o.filtered=J.stack(a,1),J.dispose(a),o.max=J.max(o.filtered,1),o.argmax=J.argMax(o.filtered,1);let l=0;o.nms=await J.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=J.slice(o.boxes,f,1),m=await d.data();J.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],b=rt(y,VA),M=[Math.trunc(y[0]*Ee[0]),Math.trunc(y[1]*Ee[1]),Math.trunc(y[2]*Ee[0]),Math.trunc(y[3]*Ee[1])],T=x[f],p=HA[i[f]],h={id:l++,score:T,box:M,boxRaw:b,label:p};n.push(h)}return Object.keys(o).forEach(f=>J.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function n1(e,t,n){let o={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&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=J.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Pt(o.keypoints);for(let i of Object.keys(no))o.annotations[i]=no[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>J.dispose(r[l]))}return o}async function o1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],jt++;let n=(t.hand.skipTime||0)>g()-t1,o=jt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-t1,l=jt<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>n1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>n1(e,x,t))):(m0.boxes=await ZA(e,t),t1=g(),m0.hands=await Promise.all(m0.boxes.map(x=>n1(e,x,t))),jt=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x<m0.hands.length;x++){let i=e3(m0.hands[x].keypoints,Ee);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=rt(i.box,to),d=rt(i.boxRaw,to);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x<m0.hands.length;x++){let i=Me(m0.hands[x].keypoints,Ee);m0.hands[x].box=i.box,m0.hands[x].boxRaw=i.boxRaw}A(m0.hands)})}var Te=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};Se(W2,{connected:()=>Nt,horizontal:()=>r1,kpt:()=>It,relative:()=>A1,vertical:()=>s1});var It=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],r1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],s1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],A1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Nt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var E=Te(),a1=0;function Ao(e,t){var A,a,l,c,x,i,f,d,m,y,b,M,T,p,h,S,P,L,V,e0,Z,_,A0,n0,t0,z0;let n=g();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(E.canvas=e.canvas),e.error&&(E.error=e.error),!E.body||e.body.length!==E.body.length)E.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let T0=e.body[v].box.map((B,X)=>((r-1)*E.body[v].box[X]+B)/r),L0=e.body[v].boxRaw.map((B,X)=>((r-1)*E.body[v].boxRaw[X]+B)/r),x0=e.body[v].keypoints.map((B,X)=>{var X0,y0,ze,k2,d2,k1,w1,E1,z1;return{score:B.score,part:B.part,position:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[0]||0)+(B.position[0]||0))/r:B.position[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[1]||0)+(B.position[1]||0))/r:B.position[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].position[2]||0)+(B.position[2]||0))/r:B.position[2]],positionRaw:[E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[0]||0)+(B.positionRaw[0]||0))/r:B.positionRaw[0],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[1]||0)+(B.positionRaw[1]||0))/r:B.positionRaw[1],E.body[v].keypoints[X]?((r-1)*(E.body[v].keypoints[X].positionRaw[2]||0)+(B.positionRaw[2]||0))/r:B.positionRaw[2]],distance:[E.body[v].keypoints[X]?((r-1)*(((X0=E.body[v].keypoints[X].distance)==null?void 0:X0[0])||0)+(((y0=B.distance)==null?void 0:y0[0])||0))/r:(ze=B.distance)==null?void 0:ze[0],E.body[v].keypoints[X]?((r-1)*(((k2=E.body[v].keypoints[X].distance)==null?void 0:k2[1])||0)+(((d2=B.distance)==null?void 0:d2[1])||0))/r:(k1=B.distance)==null?void 0:k1[1],E.body[v].keypoints[X]?((r-1)*(((w1=E.body[v].keypoints[X].distance)==null?void 0:w1[2])||0)+(((E1=B.distance)==null?void 0:E1[2])||0))/r:(z1=B.distance)==null?void 0:z1[2]]}}),Y={},w={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?w=at:(a=t.body.modelPath)!=null&&a.includes("blazepose")?w=nt:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(w=W2);for(let[B,X]of Object.entries(w.connected)){let X0=[];for(let y0=0;y0<X.length-1;y0++){let ze=x0.find(d2=>d2.part===X[y0]),k2=x0.find(d2=>d2.part===X[y0+1]);ze&&k2&&X0.push([ze.position,k2.position])}Y[B]=X0}E.body[v]={...e.body[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.hand||e.hand.length!==E.hand.length)E.hand=JSON.parse(JSON.stringify(e.hand));else for(let v=0;v<e.hand.length;v++){let T0=e.hand[v].box.map((w,B)=>((r-1)*E.hand[v].box[B]+w)/r),L0=e.hand[v].boxRaw.map((w,B)=>((r-1)*E.hand[v].boxRaw[B]+w)/r);E.hand[v].keypoints.length!==e.hand[v].keypoints.length&&(E.hand[v].keypoints=e.hand[v].keypoints);let x0=e.hand[v].keypoints&&e.hand[v].keypoints.length>0?e.hand[v].keypoints.map((w,B)=>w.map((X,X0)=>((r-1)*(E.hand[v].keypoints[B][X0]||1)+(X||0))/r)):[],Y={};if(Object.keys(E.hand[v].annotations).length!==Object.keys(e.hand[v].annotations).length)E.hand[v].annotations=e.hand[v].annotations,Y=E.hand[v].annotations;else if(e.hand[v].annotations)for(let w of Object.keys(e.hand[v].annotations))Y[w]=(i=(x=(c=e.hand[v])==null?void 0:c.annotations)==null?void 0:x[w])!=null&&i[0]?e.hand[v].annotations[w].map((B,X)=>B.map((X0,y0)=>((r-1)*E.hand[v].annotations[w][X][y0]+X0)/r)):null;E.hand[v]={...e.hand[v],box:T0,boxRaw:L0,keypoints:x0,annotations:Y}}if(!E.face||e.face.length!==E.face.length)E.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let T0=e.face[v].box.map((Y,w)=>((r-1)*E.face[v].box[w]+Y)/r),L0=e.face[v].boxRaw.map((Y,w)=>((r-1)*E.face[v].boxRaw[w]+Y)/r),x0=e.face[v].annotations;if(Object.keys(E.face[v].annotations).length!==Object.keys(e.face[v].annotations).length)E.face[v].annotations=e.face[v].annotations,x0=E.face[v].annotations;else if(e.face[v].annotations)for(let Y of Object.keys(e.face[v].annotations))x0[Y]=(m=(d=(f=e.face[v])==null?void 0:f.annotations)==null?void 0:d[Y])!=null&&m[0]?e.face[v].annotations[Y].map((w,B)=>w.map((X,X0)=>((r-1)*E.face[v].annotations[Y][B][X0]+X)/r)):null;if(e.face[v].rotation){let Y={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Y.matrix=(y=e.face[v].rotation)==null?void 0:y.matrix,Y.angle={roll:((r-1)*(((M=(b=E.face[v].rotation)==null?void 0:b.angle)==null?void 0:M.roll)||0)+(((p=(T=e.face[v].rotation)==null?void 0:T.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((S=(h=E.face[v].rotation)==null?void 0:h.angle)==null?void 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n=(t.body.skipTime||0)>g()-c2.last,o=d1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?c2.bodies:new Promise(async s=>{let A={};d1=0,A.input=yo(e,Lt),A.res=b0==null?void 0:b0.execute(A.input),c2.last=g();let a=await A.res.array();c2.bodies=A.res.shape[2]===17?KA(a,t,e):JA(a,t,e);for(let l of c2.bodies)fo(l,[e.shape[2]||1,e.shape[1]||1]),xo(l.keypoints);Object.keys(A).forEach(l=>mo.dispose(A[l])),s(c2.bodies)})}var E0=G(H());var le,Ct=[],ho=0,y1=Number.MAX_SAFE_INTEGER,Dt=0,Wt=2.5;async function bo(e){if(!le||R.initial){le=await N(e.object.modelPath);let t=le!=null&&le.executor?Object.values(le.modelSignature.inputs):void 0;Dt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&u("cached model:",le.modelUrl);return le}async function QA(e,t,n){var c,x;let o=0,r=[],s=Dt;for(let i of[1,2,4]){let f=i*13,d=E0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)===f2.length)),m=await d.array(),y=E0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)<f2.length)),b=E0.reshape(y,[-1,4,(((c=y.shape)==null?void 0:c[1])||0)/4]),M=E0.argMax(b,2),T=await M.array();for(let p=0;p<d.shape[0];p++)for(let h=0;h<(((x=d.shape)==null?void 0:x[1])||0);h++){let S=m[p][h];if(S>(n.object.minConfidence||0)&&h!==61){let P=(.5+Math.trunc(p%f))/f,L=(.5+Math.trunc(p/f))/f,V=T[p].map(v=>v*(f/i/s)),[e0,Z]=[P-Wt/i*V[0],L-Wt/i*V[1]],[_,A0]=[P+Wt/i*V[2]-e0,L+Wt/i*V[3]-Z],n0=[e0,Z,_,A0];n0=n0.map(v=>Math.max(0,Math.min(v,1)));let t0=[n0[0]*t[0],n0[1]*t[1],n0[2]*t[0],n0[3]*t[1]],z0={id:o++,score:Math.round(100*S)/100,class:h+1,label:f2[h].label,box:t0.map(v=>Math.trunc(v)),boxRaw:n0};r.push(z0)}}E0.dispose([d,y,b,M])}let A=r.map(i=>[i.boxRaw[1],i.boxRaw[0],i.boxRaw[3],i.boxRaw[2]]),a=r.map(i=>i.score),l=[];if(A&&A.length>0){let i=await E0.image.nonMaxSuppressionAsync(A,a,n.object.maxDetected||0,n.object.iouThreshold,n.object.minConfidence);l=Array.from(await i.data()),E0.dispose(i)}return r=r.filter((i,f)=>l.includes(f)).sort((i,f)=>f.score-i.score),r}async function f1(e,t){if(!(le!=null&&le.executor))return[];let n=(t.object.skipTime||0)>g()-ho,o=y1<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&Ct.length>0?(y1++,Ct):(y1=0,!R.kernels.includes("mod")||!R.kernels.includes("sparsetodense")?Ct:new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=E0.image.resizeBilinear(e,[Dt,Dt],!1),a=E0.div(A,C.tf255),l=E0.transpose(a,[0,3,1,2]),c;t.object.enabled&&(c=le.execute(l)),ho=g();let x=await QA(c,s,t);Ct=x,E0.dispose([A,a,l,...c]),r(x)}))}var H0=G(H());var F2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_A=F2.length,D2=F2.reduce((e,t,n)=>(e[t]=n,e),{}),$A=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Vi=$A.map(([e,t])=>[D2[e],D2[t]]),To=[["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 vo(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,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 Ro(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,x)=>({id:x,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:f,position:d})=>({score:i,part:f,position:[Math.trunc(d.x*A),Math.trunc(d.y*s)],positionRaw:[d.x/o,d.y/o]})),annotations:{}});return e.map((c,x)=>a(c,x))}var Ft=class{constructor(t,n){k(this,"priorityQueue");k(this,"numberOfElements");k(this,"getElementValue");this.priorityQueue=new 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d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function ma(e){let t;return typeof createImageBitmap=="function"?t=await xa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ya(e):t=await fa(e),t}async function pa(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],p=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let b=0;b<d.length;b++)d[b]===-1&&(d[b]=b===0?1:64);let y=d0.zeros(d,p);try{let b=f.execute(y);r.push(i),Array.isArray(b)?b.forEach(M=>d0.dispose(M)):d0.dispose(b)}catch(b){e.config.debug&&u("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&u("tensor leak:",A-o)}async function Co(e,t){await N2(e,!1);let n=g();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await pa(e);let r=await ma(e),s=g();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var k2,G2,V2,Zt,Ye,k1=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env",R);k(this,"draw",nt);k(this,"match",Ot);k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");ue(this,k2,void 0);ue(this,G2,void 0);ue(this,V2,void 0);k(this,"analyze",(...t)=>{if(!q0(this,G2))return;let n=this.tf.engine().state.numTensors,o=q0(this,k2);Re(this,k2,n);let r=n-o;r!==0&&u(...t,r)});ue(this,Zt,t=>{if(!q0(this,V2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ce.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"webcam",new K2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(ce.version.tfjs||ce.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=e5,Object.defineProperty(this,"version",{value:e5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),Z1(this.config),this.tf=ce,this.state="idle",Re(this,k2,0),Re(this,G2,!1),Re(this,V2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2(this),a5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=C3,this.faceUVMap=W3,Ht(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,_t(),R.initial=!0}validate(t){let n=Kt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return U2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await U2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await jo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await ao(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await No(o.tensor,this.config)),ce.dispose(o.tensor),r}compare(t,n){return V1(this.config,t,n)}async init(){await N2(this,!0),await this.tf.ready(),_t()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await N2(this,!1)||u("error: backend check failed"),await ce.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return Ao(t,this.config)}async warmup(t){let n=g(),o=await Co(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,M,T,m,h,S,P,N,Z,e0,G,_,A0,n0,t0,z0,v,T0,L0,x0,Y;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=q0(this,Zt).call(this,t);s&&(u(s,t),this.emit("error"),o(Te(s)));let A=g();await this.load(),r=g(),this.state="image";let a=await U2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=g(),this.config.skipAllowed=await G1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?J5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await J5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),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 f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?g1(a.tensor,f):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?x5(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?c=this.config.body.enabled?b5(a.tensor,f):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?y1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=g(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?await g1(a.tensor,f):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?c=this.config.body.enabled?await x5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await b5(a.tensor,f):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(c=this.config.body.enabled?await y1(a.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((e0=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&e0.includes("handdetect")?x=this.config.hand.enabled?t1(a.tensor,d):[]:(_=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&_.includes("handtrack")&&(x=this.config.hand.enabled?r1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(n0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&n0.includes("handdetect")?x=this.config.hand.enabled?await t1(a.tensor,d):[]:(z0=(t0=this.config.hand.detector)==null?void 0:t0.modelPath)!=null&&z0.includes("handtrack")&&(x=this.config.hand.enabled?await r1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((v=this.config.object.modelPath)!=null&&v.includes("nanodet")?i=this.config.object.enabled?m1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?m5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(L0=this.config.object.modelPath)!=null&&L0.includes("nanodet")?i=this.config.object.enabled?await m1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await m5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(r=g(),p=[...Ln(l),...On(c),...Wn(x),...Cn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let y=((Y=this.process.tensor)==null?void 0:Y.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:p,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Lo(l,c,x,p,y)}},ce.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(q0(this,Ye)[t.id]||(this.config.debug&&u("video start",t.id),q0(this,Ye)[t.id]=!0),!t.paused&&q0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),q0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),q0(this,Ye)[t.id]=!1)}};k2=new WeakMap,G2=new WeakMap,V2=new WeakMap,Zt=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models});
+2Q==`;async function xa(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(A=>A.blob()),n,o;switch(e.config.warmup){case"face":n=await t(Gt);break;case"body":case"full":n=await t(Vt);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function ya(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+Gt;break;case"full":case"body":n="data:image/jpeg;base64,"+Vt;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(R.Image)o=new R.Image;else{t(void 0);return}o.onload=async()=>{let r=se(o.naturalWidth,o.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function fa(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(Gt):n=t(Vt);let o;if("node"in d0&&d0.getBackend()==="tensorflow"){let r=d0.node.decodeJpeg(n),s=d0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function ma(e){let t;return typeof createImageBitmap=="function"?t=await xa(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await ya(e):t=await fa(e),t}async function pa(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,f]of Object.entries(e.models.models)){if(!f)continue;let d=f!=null&&f.modelSignature&&((l=(a=f==null?void 0:f.inputs)==null?void 0:a[0])!=null&&l.shape)?[...f.inputs[0].shape]:[1,64,64,3],m=f!=null&&f.modelSignature&&((x=(c=f==null?void 0:f.inputs)==null?void 0:c[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let b=0;b<d.length;b++)d[b]===-1&&(d[b]=b===0?1:64);let y=d0.zeros(d,m);try{let b=f.execute(y);r.push(i),Array.isArray(b)?b.forEach(M=>d0.dispose(M)):d0.dispose(b)}catch(b){e.config.debug&&u("compile fail model:",i)}d0.dispose(y)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&u("tensor leak:",A-o)}async function Co(e,t){await I2(e,!1);let n=g();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?Te():new Promise(async o=>{await e.models.load(),await pa(e);let r=await ma(e),s=g();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,H2,G2,Zt,Ye,P1=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env",R);k(this,"draw",tt);k(this,"match",Ot);k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");ue(this,P2,void 0);ue(this,H2,void 0);ue(this,G2,void 0);k(this,"analyze",(...t)=>{if(!q0(this,H2))return;let n=this.tf.engine().state.numTensors,o=q0(this,P2);Re(this,P2,n);let r=n-o;r!==0&&u(...t,r)});ue(this,Zt,t=>{if(!q0(this,G2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ce.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"webcam",new Y2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(ce.version.tfjs||ce.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=e5,Object.defineProperty(this,"version",{value:e5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),V1(this.config),this.tf=ce,this.state="idle",Re(this,P2,0),Re(this,H2,!1),Re(this,G2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new B2(this),a5(),this.result=Te(),this.process={tensor:null,canvas:null},this.faceTriangulation=C3,this.faceUVMap=W3,Ht(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,_t(),R.initial=!0}validate(t){let n=Kt(Ke,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return q2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await q2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await jo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await ao(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await No(o.tensor,this.config)),ce.dispose(o.tensor),r}compare(t,n){return G1(this.config,t,n)}async init(){await I2(this,!0),await this.tf.ready(),_t()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await I2(this,!1)||u("error: backend check failed"),await ce.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return Ao(t,this.config)}async warmup(t){let n=g(),o=await Co(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,M,T,p,h,S,P,L,V,e0,Z,_,A0,n0,t0,z0,v,T0,L0,x0,Y;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=q0(this,Zt).call(this,t);s&&(u(s,t),this.emit("error"),o(Te(s)));let A=g();await this.load(),r=g(),this.state="image";let a=await q2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o(Te("could not convert input to tensor"));return}this.emit("image"),r=g(),this.config.skipAllowed=await H1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,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()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?K5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await K5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),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 f=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?b1(a.tensor,f):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?x5(a.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?c=this.config.body.enabled?b5(a.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?x1(a.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=g(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?await b1(a.tensor,f):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?c=this.config.body.enabled?await x5(a.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await b5(a.tensor,f):[]:(L=this.config.body.modelPath)!=null&&L.includes("movenet")&&(c=this.config.body.enabled?await x1(a.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((e0=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&e0.includes("handdetect")?x=this.config.hand.enabled?e1(a.tensor,d):[]:(_=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&_.includes("handtrack")&&(x=this.config.hand.enabled?o1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(n0=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&n0.includes("handdetect")?x=this.config.hand.enabled?await e1(a.tensor,d):[]:(z0=(t0=this.config.hand.detector)==null?void 0:t0.modelPath)!=null&&z0.includes("handtrack")&&(x=this.config.hand.enabled?await o1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((v=this.config.object.modelPath)!=null&&v.includes("nanodet")?i=this.config.object.enabled?f1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?m5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(L0=this.config.object.modelPath)!=null&&L0.includes("nanodet")?i=this.config.object.enabled?await f1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await m5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=g(),m=[...Ln(l),...On(c),...Wn(x),...Cn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let y=((Y=this.process.tensor)==null?void 0:Y.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:y[2],height:y[1],get persons(){return Lo(l,c,x,m,y)}},ce.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(q0(this,Ye)[t.id]||(this.config.debug&&u("video start",t.id),q0(this,Ye)[t.id]=!0),!t.paused&&q0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),q0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),q0(this,Ye)[t.id]=!1)}};P2=new WeakMap,H2=new WeakMap,G2=new WeakMap,Zt=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models});
diff --git a/package.json b/package.json
index 9daa6fa8..de2e3707 100644
--- a/package.json
+++ b/package.json
@@ -80,24 +80,26 @@
   "devDependencies": {
     "@html-eslint/eslint-plugin": "^0.16.0",
     "@html-eslint/parser": "^0.16.0",
-    "@microsoft/api-extractor": "^7.34.2",
+    "@microsoft/api-extractor": "^7.34.3",
     "@tensorflow/tfjs-backend-cpu": "^4.2.0",
     "@tensorflow/tfjs-backend-wasm": "^4.2.0",
     "@tensorflow/tfjs-backend-webgl": "^4.2.0",
     "@tensorflow/tfjs-backend-webgpu": "0.0.1-alpha.17",
     "@tensorflow/tfjs-converter": "^4.2.0",
     "@tensorflow/tfjs-core": "^4.2.0",
+    "@tensorflow/tfjs-data": "^4.2.0",
+    "@tensorflow/tfjs-layers": "^4.2.0",
     "@tensorflow/tfjs-node": "^4.2.0",
     "@tensorflow/tfjs-node-gpu": "^4.2.0",
-    "@types/node": "^18.11.18",
+    "@types/node": "^18.11.19",
     "@types/offscreencanvas": "^2019.7.0",
-    "@typescript-eslint/eslint-plugin": "^5.50.0",
-    "@typescript-eslint/parser": "^5.50.0",
-    "@vladmandic/build": "^0.7.15",
+    "@typescript-eslint/eslint-plugin": "^5.51.0",
+    "@typescript-eslint/parser": "^5.51.0",
+    "@vladmandic/build": "0.7.15",
     "@vladmandic/pilogger": "^0.4.7",
     "@vladmandic/tfjs": "github:vladmandic/tfjs",
     "canvas": "^2.11.0",
-    "esbuild": "^0.17.5",
+    "esbuild": "^0.17.6",
     "eslint": "8.33.0",
     "eslint-config-airbnb-base": "^15.0.0",
     "eslint-plugin-html": "^7.1.0",
diff --git a/src/exports.ts b/src/exports.ts
index 3852a090..a52432f5 100644
--- a/src/exports.ts
+++ b/src/exports.ts
@@ -9,7 +9,7 @@ export * from './result';
 /**
  * Explict reexport of main @tensorflow/tfjs types
  */
-export type { Tensor, Tensor4D, GraphModel, Rank } from './tfjs/types';
+export type { Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorLike, GraphModel, Rank } from './tfjs/types';
 
 // re-export types
 export type { DrawOptions } from './draw/options';
diff --git a/src/face/blazeface.ts b/src/face/blazeface.ts
index 264fd310..d597d9d4 100644
--- a/src/face/blazeface.ts
+++ b/src/face/blazeface.ts
@@ -82,6 +82,7 @@ export async function getBoxes(inputImage: Tensor4D, config: Config): Promise<De
   const scores = await t.scores.data();
   for (let i = 0; i < nms.length; i++) {
     const confidence = scores[nms[i]];
+
     if (confidence > (config.face.detector?.minConfidence || 0)) {
       const b: Record<string, Tensor> = {};
       b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);
@@ -95,7 +96,9 @@ export async function getBoxes(inputImage: Tensor4D, config: Config): Promise<De
         landmarks: (await b.landmarks.array()) as Point[],
         confidence,
       };
-      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);
+      b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);
+      const anchor = await b.anchor.data();
+      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);
       const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);
       const squaredBox = util.squarifyBox(enlargedBox);
       if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) boxes.push(squaredBox);
diff --git a/src/face/facemesh.ts b/src/face/facemesh.ts
index c2823176..1c1237ca 100644
--- a/src/face/facemesh.ts
+++ b/src/face/facemesh.ts
@@ -78,10 +78,7 @@ export async function predict(input: Tensor4D, config: Config): Promise<FaceResu
       face.boxRaw = util.getRawBox(box, input);
       face.score = face.boxScore;
       face.size = box.size;
-      face.mesh = box.landmarks.map((pt) => [
-        ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),
-        ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),
-      ]);
+      face.mesh = box.landmarks;
       face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);
       for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations
     } else if (!model) { // mesh enabled, but not loaded
@@ -98,14 +95,11 @@ export async function predict(input: Tensor4D, config: Config): Promise<FaceResu
       face.faceScore = Math.round(100 * faceConfidence[0]) / 100;
       if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh
         box.confidence = face.faceScore; // reset confidence of cached box
-        if (config.face.mesh.keepInvalid) {
+        if (config.face.mesh['keepInvalid']) {
           face.box = util.clampBox(box, input);
           face.boxRaw = util.getRawBox(box, input);
           face.score = face.boxScore;
-          face.mesh = box.landmarks.map((pt) => [
-            ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),
-            ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),
-          ]);
+          face.mesh = box.landmarks;
           face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);
           for (const key of Object.keys(coords.blazeFaceLandmarks)) {
             face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations
diff --git a/src/face/facemeshutil.ts b/src/face/facemeshutil.ts
index 66e17a19..02023162 100644
--- a/src/face/facemeshutil.ts
+++ b/src/face/facemeshutil.ts
@@ -31,10 +31,18 @@ export const getRawBox = (box, input): Box => (box ? [
   (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),
 ] : [0, 0, 0, 0]);
 
-export const scaleBoxCoordinates = (box, factor) => {
+export const scaleBoxCoordinates = (box, factor, anchor) => {
   const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
   const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
-  const landmarks = box.landmarks;
+  // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];
+  const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);
+  /**
+  face.mesh = box.landmarks.map((pt) => [
+    ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),
+    ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),
+  ]);
+  */
+
   return { startPoint, endPoint, landmarks, confidence: box.confidence };
 };
 
diff --git a/src/image/enhance.ts b/src/image/enhance.ts
index 9fe846e5..dc3e098e 100644
--- a/src/image/enhance.ts
+++ b/src/image/enhance.ts
@@ -3,7 +3,7 @@
  */
 
 import * as tf from 'dist/tfjs.esm.js';
-import type { Tensor } from '../exports';
+import type { Tensor } from '../tfjs/types';
 
 export async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {
   const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
diff --git a/test/build.log b/test/build.log
index d21c4732..4310f596 100644
--- a/test/build.log
+++ b/test/build.log
@@ -1,50 +1,50 @@
-2023-02-03 10:01:19 DATA:  Build {"name":"@vladmandic/human","version":"3.0.5"} 
-2023-02-03 10:01:19 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.5"} 
-2023-02-03 10:01:19 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2023-02-03 10:01:19 INFO:  Toolchain: {"build":"0.7.15","esbuild":"0.16.17","typescript":"4.9.5","typedoc":"0.23.24","eslint":"8.33.0"} 
-2023-02-03 10:01:19 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2023-02-03 10:01:19 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} 
-2023-02-03 10:01:19 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} 
-2023-02-03 10:01:19 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":674069,"outputBytes":319121} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":674073,"outputBytes":319125} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":675021,"outputBytes":319236} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":673815,"outputBytes":317693} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1151306} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1824451,"outputBytes":1464941} 
-2023-02-03 10:01:20 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1824451,"outputBytes":1931859} 
-2023-02-03 10:01:24 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} 
-2023-02-03 10:01:26 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":77,"generated":true} 
-2023-02-03 10:01:26 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6117,"outputBytes":2901} 
-2023-02-03 10:01:27 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17503,"outputBytes":9403} 
-2023-02-03 10:01:36 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":170,"errors":0,"warnings":0} 
-2023-02-03 10:01:36 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2023-02-03 10:01:36 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} 
-2023-02-03 10:01:36 INFO:  Done... 
-2023-02-03 10:01:36 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} 
-2023-02-03 10:01:36 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} 
-2023-02-03 10:01:36 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} 
-2023-02-03 10:01:36 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} 
-2023-02-03 10:01:36 STATE: Filter: {"input":"types/tfjs-core.d.ts"} 
-2023-02-03 10:01:38 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":204} 
-2023-02-03 10:01:38 STATE: Filter: {"input":"types/human.d.ts"} 
-2023-02-03 10:01:38 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} 
-2023-02-03 10:01:38 STATE: Write: {"output":"dist/human.esm.d.ts"} 
-2023-02-03 10:01:38 STATE: Write: {"output":"dist/human.d.ts"} 
-2023-02-03 10:01:38 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} 
-2023-02-03 10:01:38 STATE: Write: {"output":"dist/human.node.d.ts"} 
-2023-02-03 10:01:38 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} 
-2023-02-03 10:01:38 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} 
-2023-02-03 10:01:38 STATE: Models {"folder":"./models","models":12} 
-2023-02-03 10:01:38 STATE: Models {"folder":"../human-models/models","models":44} 
-2023-02-03 10:01:38 STATE: Models {"folder":"../blazepose/model/","models":4} 
-2023-02-03 10:01:38 STATE: Models {"folder":"../anti-spoofing/model","models":1} 
-2023-02-03 10:01:38 STATE: Models {"folder":"../efficientpose/models","models":3} 
-2023-02-03 10:01:38 STATE: Models {"folder":"../insightface/models","models":5} 
-2023-02-03 10:01:38 STATE: Models {"folder":"../movenet/models","models":3} 
-2023-02-03 10:01:38 STATE: Models {"folder":"../nanodet/models","models":4} 
-2023-02-03 10:01:38 STATE: Models: {"count":58,"totalSize":380063249} 
-2023-02-03 10:01:38 INFO:  Human Build complete... {"logFile":"test/build.log"} 
+2023-02-06 14:26:30 DATA:  Build {"name":"@vladmandic/human","version":"3.0.5"} 
+2023-02-06 14:26:30 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.5"} 
+2023-02-06 14:26:30 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2023-02-06 14:26:30 INFO:  Toolchain: {"build":"0.7.15","esbuild":"0.16.17","typescript":"4.9.5","typedoc":"0.23.24","eslint":"8.33.0"} 
+2023-02-06 14:26:30 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
+2023-02-06 14:26:30 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":674251,"outputBytes":318951} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":674255,"outputBytes":318955} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":675203,"outputBytes":319066} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":673997,"outputBytes":317523} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1151306} 
+2023-02-06 14:26:30 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1824633,"outputBytes":1464766} 
+2023-02-06 14:26:31 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1824633,"outputBytes":1931611} 
+2023-02-06 14:26:35 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} 
+2023-02-06 14:26:37 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} 
+2023-02-06 14:26:37 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6235,"outputBytes":2901} 
+2023-02-06 14:26:37 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17503,"outputBytes":9403} 
+2023-02-06 14:26:46 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":170,"errors":0,"warnings":0} 
+2023-02-06 14:26:46 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2023-02-06 14:26:46 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} 
+2023-02-06 14:26:46 INFO:  Done... 
+2023-02-06 14:26:46 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} 
+2023-02-06 14:26:46 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} 
+2023-02-06 14:26:46 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} 
+2023-02-06 14:26:46 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} 
+2023-02-06 14:26:46 STATE: Filter: {"input":"types/tfjs-core.d.ts"} 
+2023-02-06 14:26:48 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":210} 
+2023-02-06 14:26:48 STATE: Filter: {"input":"types/human.d.ts"} 
+2023-02-06 14:26:48 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} 
+2023-02-06 14:26:48 STATE: Write: {"output":"dist/human.esm.d.ts"} 
+2023-02-06 14:26:48 STATE: Write: {"output":"dist/human.d.ts"} 
+2023-02-06 14:26:48 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} 
+2023-02-06 14:26:48 STATE: Write: {"output":"dist/human.node.d.ts"} 
+2023-02-06 14:26:48 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} 
+2023-02-06 14:26:48 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} 
+2023-02-06 14:26:48 STATE: Models {"folder":"./models","models":12} 
+2023-02-06 14:26:48 STATE: Models {"folder":"../human-models/models","models":44} 
+2023-02-06 14:26:48 STATE: Models {"folder":"../blazepose/model/","models":4} 
+2023-02-06 14:26:48 STATE: Models {"folder":"../anti-spoofing/model","models":1} 
+2023-02-06 14:26:48 STATE: Models {"folder":"../efficientpose/models","models":3} 
+2023-02-06 14:26:48 STATE: Models {"folder":"../insightface/models","models":5} 
+2023-02-06 14:26:48 STATE: Models {"folder":"../movenet/models","models":3} 
+2023-02-06 14:26:48 STATE: Models {"folder":"../nanodet/models","models":4} 
+2023-02-06 14:26:48 STATE: Models: {"count":58,"totalSize":380063249} 
+2023-02-06 14:26:48 INFO:  Human Build complete... {"logFile":"test/build.log"} 
diff --git a/test/test.log b/test/test.log
index 4c55d4fa..3e3c553f 100644
--- a/test/test.log
+++ b/test/test.log
@@ -1,1004 +1,1003 @@
-2023-02-03 10:01:43 INFO:  @vladmandic/human version 3.0.5 
-2023-02-03 10:01:43 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 
-2023-02-03 10:01:43 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]},{"cmd":"../test/test-node-emotion.js","args":[]}] 
-2023-02-03 10:01:43 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start 
-2023-02-03 10:01:44 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start 
-2023-02-03 10:01:45 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start 
-2023-02-03 10:01:46 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start 
-2023-02-03 10:01:46 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start 
-2023-02-03 10:01:47 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start 
-2023-02-03 10:01:49 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start 
-2023-02-03 10:02:03 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start 
-2023-02-03 10:02:04 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start 
-2023-02-03 10:02:21 INFO:  {"cmd":"../test/test-node-emotion.js","args":[]} start 
-2023-02-03 10:02:28 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] 
-2023-02-03 10:02:28 INFO:  
-2023-02-03 10:02:28 INFO:  test-node-load.js start 
-2023-02-03 10:02:28 INFO:  test-node-load.js load start {"human":"3.0.5","tf":"4.2.0","progress":0} 
-2023-02-03 10:02:28 DATA:  test-node-load.js load interval {"elapsed":1,"progress":0} 
-2023-02-03 10:02:28 DATA:  test-node-load.js load interval {"elapsed":11,"progress":0} 
-2023-02-03 10:02:28 DATA:  test-node-load.js load interval {"elapsed":22,"progress":0.06164218554150753} 
-2023-02-03 10:02:28 DATA:  test-node-load.js load interval {"elapsed":34,"progress":0.12865823126207815} 
-2023-02-03 10:02:28 DATA:  test-node-load.js load interval {"elapsed":47,"progress":0.33826045252170456} 
-2023-02-03 10:02:28 DATA:  test-node-load.js load interval {"elapsed":75,"progress":0.4726970548784964} 
-2023-02-03 10:02:28 DATA:  test-node-load.js load interval {"elapsed":90,"progress":0.68355493315821} 
-2023-02-03 10:02:28 STATE: test-node-load.js passed {"progress":1} 
-2023-02-03 10:02:28 INFO:  test-node-load.js load final {"progress":1} 
-2023-02-03 10:02:29 DATA:  test-node-load.js load interval {"elapsed":504,"progress":1} 
-2023-02-03 10:02:29 INFO:  
-2023-02-03 10:02:29 INFO:  test-node-gear.js start 
-2023-02-03 10:02:29 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] 
-2023-02-03 10:02:30 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg 
-2023-02-03 10:02:30 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92,"emotion":[]} 
-2023-02-03 10:02:30 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg 
-2023-02-03 10:02:30 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}],"emotion":[]} 
-2023-02-03 10:02:30 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg 
-2023-02-03 10:02:30 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99,"emotion":[]} 
-2023-02-03 10:02:30 INFO:  
-2023-02-03 10:02:30 INFO:  test-backend-node.js start 
-2023-02-03 10:02:31 INFO:  test-backend-node.js test: configuration validation 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: configuration default validation [] 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2023-02-03 10:02:31 INFO:  test-backend-node.js test: model load 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] 
-2023-02-03 10:02:31 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} 
-2023-02-03 10:02:31 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
-2023-02-03 10:02:31 INFO:  test-backend-node.js test: warmup 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: create human 
-2023-02-03 10:02:31 INFO:  test-backend-node.js human version: 3.0.5 
-2023-02-03 10:02:31 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v19.1.0 
-2023-02-03 10:02:31 INFO:  test-backend-node.js tfjs version: 4.2.0 
-2023-02-03 10:02:31 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.2.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: set backend: tensorflow 
-2023-02-03 10:02:31 STATE: test-backend-node.js tensors 1785 
-2023-02-03 10:02:31 STATE: test-backend-node.js  result: defined models: 11 loaded models: 11 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: load models 11 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: warmup: none default 
-2023-02-03 10:02:31 DATA:  test-backend-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2023-02-03 10:02:31 DATA:  test-backend-node.js  result: performance: load: null total: null 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: warmup none result match 
-2023-02-03 10:02:31 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:31 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:31 STATE: test-backend-node.js event: warmup 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: warmup: face default 
-2023-02-03 10:02:31 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2023-02-03 10:02:31 DATA:  test-backend-node.js  result: performance: load: null total: 451 
-2023-02-03 10:02:31 STATE: test-backend-node.js passed: warmup face result match 
-2023-02-03 10:02:31 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:32 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:32 STATE: test-backend-node.js event: warmup 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: warmup: body default 
-2023-02-03 10:02:32 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:32 DATA:  test-backend-node.js  result: performance: load: null total: 389 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: warmup body result match 
-2023-02-03 10:02:32 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2023-02-03 10:02:32 INFO:  test-backend-node.js test: details verification 
-2023-02-03 10:02:32 STATE: test-backend-node.js start default 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:32 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:32 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default 
-2023-02-03 10:02:32 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:32 DATA:  test-backend-node.js  result: performance: load: null total: 344 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details face length 1 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details face score 1 0.93 1 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details face arrays 4 478 1024 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details face liveness 0.83 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details body length 1 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details body 0.92 17 6 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details hand length 1 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details hand 0.51 0.73 point 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details hand arrays 21 5 7 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details gesture length 7 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details object length 1 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: details object 0.72 person 
-2023-02-03 10:02:32 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2023-02-03 10:02:32 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:33 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:33 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2023-02-03 10:02:33 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2023-02-03 10:02:33 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:33 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:33 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2023-02-03 10:02:34 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:34 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:34 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:34 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2023-02-03 10:02:34 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:34 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:34 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:34 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2023-02-03 10:02:35 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2023-02-03 10:02:35 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:35 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:35 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2023-02-03 10:02:35 INFO:  test-backend-node.js test default 
-2023-02-03 10:02:35 STATE: test-backend-node.js start async 
-2023-02-03 10:02:35 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:35 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:36 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:36 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async 
-2023-02-03 10:02:36 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:36 DATA:  test-backend-node.js  result: performance: load: null total: 367 
-2023-02-03 10:02:36 STATE: test-backend-node.js passed: default result face match 1 female 0.97 
-2023-02-03 10:02:36 INFO:  test-backend-node.js test sync 
-2023-02-03 10:02:36 STATE: test-backend-node.js start sync 
-2023-02-03 10:02:36 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:36 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:36 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:36 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync 
-2023-02-03 10:02:36 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:36 DATA:  test-backend-node.js  result: performance: load: null total: 369 
-2023-02-03 10:02:36 STATE: test-backend-node.js passed: default sync 1 female 0.97 
-2023-02-03 10:02:36 INFO:  test-backend-node.js test: image process 
-2023-02-03 10:02:36 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:02:36 STATE: test-backend-node.js passed: image input null [1,256,256,3] 
-2023-02-03 10:02:36 INFO:  test-backend-node.js test: image null 
-2023-02-03 10:02:36 STATE: test-backend-node.js passed: invalid input could not convert input to tensor 
-2023-02-03 10:02:36 INFO:  test-backend-node.js test face similarity 
-2023-02-03 10:02:36 STATE: test-backend-node.js start face similarity 
-2023-02-03 10:02:36 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:02:36 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:37 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:37 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity 
-2023-02-03 10:02:37 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2023-02-03 10:02:37 DATA:  test-backend-node.js  result: performance: load: null total: 347 
-2023-02-03 10:02:37 STATE: test-backend-node.js start face similarity 
-2023-02-03 10:02:37 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:37 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:37 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:37 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity 
-2023-02-03 10:02:37 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:37 DATA:  test-backend-node.js  result: performance: load: null total: 343 
-2023-02-03 10:02:37 STATE: test-backend-node.js start face similarity 
-2023-02-03 10:02:37 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2023-02-03 10:02:37 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:38 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:38 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity 
-2023-02-03 10:02:38 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2023-02-03 10:02:38 DATA:  test-backend-node.js  result: performance: load: null total: 296 
-2023-02-03 10:02:38 STATE: test-backend-node.js passed: face descriptor 
-2023-02-03 10:02:38 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} 
-2023-02-03 10:02:38 INFO:  test-backend-node.js test object 
-2023-02-03 10:02:38 STATE: test-backend-node.js start object 
-2023-02-03 10:02:38 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:38 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:38 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:38 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object 
-2023-02-03 10:02:38 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:38 DATA:  test-backend-node.js  result: performance: load: null total: 330 
-2023-02-03 10:02:38 STATE: test-backend-node.js passed: centernet 
-2023-02-03 10:02:38 STATE: test-backend-node.js start object 
-2023-02-03 10:02:39 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:39 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:40 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:40 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object 
-2023-02-03 10:02:40 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:40 DATA:  test-backend-node.js  result: performance: load: null total: 322 
-2023-02-03 10:02:40 STATE: test-backend-node.js passed: nanodet 
-2023-02-03 10:02:40 INFO:  test-backend-node.js test sensitive 
-2023-02-03 10:02:40 STATE: test-backend-node.js start sensitive 
-2023-02-03 10:02:40 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:40 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:40 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:40 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive 
-2023-02-03 10:02:40 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:40 DATA:  test-backend-node.js  result: performance: load: null total: 301 
-2023-02-03 10:02:40 STATE: test-backend-node.js passed: sensitive result match 
-2023-02-03 10:02:40 STATE: test-backend-node.js passed: sensitive face result match 
-2023-02-03 10:02:40 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2023-02-03 10:02:40 STATE: test-backend-node.js passed: sensitive body result match 
-2023-02-03 10:02:40 STATE: test-backend-node.js passed: sensitive hand result match 
-2023-02-03 10:02:40 INFO:  test-backend-node.js test body 
-2023-02-03 10:02:40 STATE: test-backend-node.js start blazepose 
-2023-02-03 10:02:43 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:43 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:43 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:43 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose 
-2023-02-03 10:02:43 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} 
-2023-02-03 10:02:43 DATA:  test-backend-node.js  result: performance: load: null total: 393 
-2023-02-03 10:02:43 STATE: test-backend-node.js passed: blazepose 
-2023-02-03 10:02:43 STATE: test-backend-node.js start efficientpose 
-2023-02-03 10:02:44 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:44 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:44 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:44 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose 
-2023-02-03 10:02:44 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} 
-2023-02-03 10:02:44 DATA:  test-backend-node.js  result: performance: load: null total: 330 
-2023-02-03 10:02:44 STATE: test-backend-node.js passed: efficientpose 
-2023-02-03 10:02:44 STATE: test-backend-node.js start posenet 
-2023-02-03 10:02:45 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:45 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:45 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:45 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet 
-2023-02-03 10:02:45 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} 
-2023-02-03 10:02:45 DATA:  test-backend-node.js  result: performance: load: null total: 276 
-2023-02-03 10:02:45 STATE: test-backend-node.js passed: posenet 
-2023-02-03 10:02:45 STATE: test-backend-node.js start movenet 
-2023-02-03 10:02:45 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:45 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:45 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:45 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet 
-2023-02-03 10:02:45 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:45 DATA:  test-backend-node.js  result: performance: load: null total: 278 
-2023-02-03 10:02:45 STATE: test-backend-node.js passed: movenet 
-2023-02-03 10:02:45 INFO:  test-backend-node.js test face matching 
-2023-02-03 10:02:45 STATE: test-backend-node.js passed: face database 40 
-2023-02-03 10:02:45 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} 
-2023-02-03 10:02:45 INFO:  test-backend-node.js test face similarity alternative 
-2023-02-03 10:02:45 STATE: test-backend-node.js start face embeddings 
-2023-02-03 10:02:46 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:02:46 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:47 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:47 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2023-02-03 10:02:47 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:02:47 DATA:  test-backend-node.js  result: performance: load: null total: 335 
-2023-02-03 10:02:47 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} 
-2023-02-03 10:02:47 STATE: test-backend-node.js start face embeddings 
-2023-02-03 10:02:48 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:02:48 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:48 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:48 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2023-02-03 10:02:48 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:02:48 DATA:  test-backend-node.js  result: performance: load: null total: 337 
-2023-02-03 10:02:48 STATE: test-backend-node.js passed: insightface {"embedding":512} 
-2023-02-03 10:02:48 INFO:  test-backend-node.js test face attention 
-2023-02-03 10:02:48 STATE: test-backend-node.js start face attention 
-2023-02-03 10:02:48 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:02:48 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:49 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:49 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention 
-2023-02-03 10:02:49 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:02:49 DATA:  test-backend-node.js  result: performance: load: null total: 314 
-2023-02-03 10:02:49 STATE: test-backend-node.js passed: face attention 
-2023-02-03 10:02:49 INFO:  test-backend-node.js test detectors 
-2023-02-03 10:02:49 STATE: test-backend-node.js start detectors 
-2023-02-03 10:02:49 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:49 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:49 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:49 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors 
-2023-02-03 10:02:49 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:49 DATA:  test-backend-node.js  result: performance: load: null total: 161 
-2023-02-03 10:02:49 STATE: test-backend-node.js passed: detector result face match 
-2023-02-03 10:02:49 STATE: test-backend-node.js passed: detector result hand match 
-2023-02-03 10:02:49 INFO:  test-backend-node.js test: multi-instance 
-2023-02-03 10:02:49 STATE: test-backend-node.js start multi instance 
-2023-02-03 10:02:49 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:49 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:49 STATE: test-backend-node.js passed: detect: random multi instance 
-2023-02-03 10:02:49 DATA:  test-backend-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
-2023-02-03 10:02:49 DATA:  test-backend-node.js  result: performance: load: null total: 154 
-2023-02-03 10:02:49 INFO:  test-backend-node.js test: first instance 
-2023-02-03 10:02:49 STATE: test-backend-node.js start multi instance 
-2023-02-03 10:02:50 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2023-02-03 10:02:50 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2023-02-03 10:02:50 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:02:50 DATA:  test-backend-node.js  result: performance: load: null total: 159 
-2023-02-03 10:02:50 INFO:  test-backend-node.js test: second instance 
-2023-02-03 10:02:50 STATE: test-backend-node.js start multi instance 
-2023-02-03 10:02:50 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2023-02-03 10:02:50 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2023-02-03 10:02:50 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:02:50 DATA:  test-backend-node.js  result: performance: load: null total: 142 
-2023-02-03 10:02:50 INFO:  test-backend-node.js test: concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js start concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js start concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js start concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js start concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js start concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js start concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js start concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js start concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js start concurrent 
-2023-02-03 10:02:50 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:02:50 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:02:50 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:02:50 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:50 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:51 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:51 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2023-02-03 10:02:51 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2023-02-03 10:02:51 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2023-02-03 10:02:51 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:51 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:51 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:52 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: performance: load: null total: 1230 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: performance: load: null total: 1230 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: performance: load: null total: 1230 
-2023-02-03 10:02:52 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:52 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: performance: load: null total: 1231 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: performance: load: null total: 1231 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: performance: load: null total: 1231 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: performance: load: null total: 1231 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: performance: load: null total: 1231 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:52 DATA:  test-backend-node.js  result: performance: load: null total: 1231 
-2023-02-03 10:02:52 INFO:  test-backend-node.js test: monkey-patch 
-2023-02-03 10:02:52 STATE: test-backend-node.js event: image 
-2023-02-03 10:02:52 STATE: test-backend-node.js event: detect 
-2023-02-03 10:02:52 STATE: test-backend-node.js passed: monkey patch 
-2023-02-03 10:02:53 STATE: test-backend-node.js passed: segmentation [262144] 
-2023-02-03 10:02:53 STATE: test-backend-node.js passeed: equal usage 
-2023-02-03 10:02:53 INFO:  test-backend-node.js test: input compare 
-2023-02-03 10:02:53 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:02:53 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2023-02-03 10:02:53 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 
-2023-02-03 10:02:53 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} 
-2023-02-03 10:02:53 INFO:  test-backend-node.js tensors 14855 
-2023-02-03 10:02:53 INFO:  test-backend-node.js test complete: 22314 ms 
-2023-02-03 10:02:53 INFO:  
-2023-02-03 10:02:53 INFO:  test-backend-node-gpu.js start 
-2023-02-03 10:02:54 INFO:  test-backend-node-gpu.js test: configuration validation 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js passed: configuration default validation [] 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2023-02-03 10:02:54 INFO:  test-backend-node-gpu.js test: model load 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] 
-2023-02-03 10:02:54 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} 
-2023-02-03 10:02:54 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
-2023-02-03 10:02:54 INFO:  test-backend-node-gpu.js test: warmup 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js passed: create human 
-2023-02-03 10:02:54 INFO:  test-backend-node-gpu.js human version: 3.0.5 
-2023-02-03 10:02:54 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v19.1.0 
-2023-02-03 10:02:54 INFO:  test-backend-node-gpu.js tfjs version: 4.2.0 
-2023-02-03 10:02:54 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.2.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":true},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js passed: set backend: tensorflow 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js tensors 1785 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js  result: defined models: 11 loaded models: 11 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js passed: load models 11 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js passed: warmup: none default 
-2023-02-03 10:02:54 DATA:  test-backend-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2023-02-03 10:02:54 DATA:  test-backend-node-gpu.js  result: performance: load: null total: null 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js passed: warmup none result match 
-2023-02-03 10:02:54 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js event: warmup 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js passed: warmup: face default 
-2023-02-03 10:02:56 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2023-02-03 10:02:56 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 1964 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js passed: warmup face result match 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js event: warmup 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js passed: warmup: body default 
-2023-02-03 10:02:56 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:56 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 155 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js passed: warmup body result match 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2023-02-03 10:02:56 INFO:  test-backend-node-gpu.js test: details verification 
-2023-02-03 10:02:56 STATE: test-backend-node-gpu.js start default 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2023-02-03 10:02:57 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:57 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 145 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details face length 1 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details body length 1 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details hand length 1 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details gesture length 7 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details object length 1 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: details object 0.72 person 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:02:57 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2023-02-03 10:02:58 INFO:  test-backend-node-gpu.js test default 
-2023-02-03 10:02:58 STATE: test-backend-node-gpu.js start async 
-2023-02-03 10:02:59 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:02:59 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:02:59 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:02:59 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async 
-2023-02-03 10:02:59 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:02:59 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 398 
-2023-02-03 10:02:59 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 
-2023-02-03 10:02:59 INFO:  test-backend-node-gpu.js test sync 
-2023-02-03 10:02:59 STATE: test-backend-node-gpu.js start sync 
-2023-02-03 10:02:59 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:02:59 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync 
-2023-02-03 10:03:00 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:00 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 409 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 
-2023-02-03 10:03:00 INFO:  test-backend-node-gpu.js test: image process 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] 
-2023-02-03 10:03:00 INFO:  test-backend-node-gpu.js test: image null 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor 
-2023-02-03 10:03:00 INFO:  test-backend-node-gpu.js test face similarity 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js start face similarity 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity 
-2023-02-03 10:03:00 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:00 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 446 
-2023-02-03 10:03:00 STATE: test-backend-node-gpu.js start face similarity 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity 
-2023-02-03 10:03:01 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:01 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 165 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js start face similarity 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity 
-2023-02-03 10:03:01 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:01 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 159 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js passed: face descriptor 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.4475006597943763,0.5570889682705169],"descriptors":[1024,1024,1024]} 
-2023-02-03 10:03:01 INFO:  test-backend-node-gpu.js test object 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js start object 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object 
-2023-02-03 10:03:01 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:01 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 171 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js passed: centernet 
-2023-02-03 10:03:01 STATE: test-backend-node-gpu.js start object 
-2023-02-03 10:03:03 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:03 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:03 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:03 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object 
-2023-02-03 10:03:03 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:03 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 712 
-2023-02-03 10:03:03 STATE: test-backend-node-gpu.js passed: nanodet 
-2023-02-03 10:03:03 INFO:  test-backend-node-gpu.js test sensitive 
-2023-02-03 10:03:03 STATE: test-backend-node-gpu.js start sensitive 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive 
-2023-02-03 10:03:04 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:04 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 325 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js passed: sensitive result match 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js passed: sensitive face result match 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js passed: sensitive body result match 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js passed: sensitive hand result match 
-2023-02-03 10:03:04 INFO:  test-backend-node-gpu.js test body 
-2023-02-03 10:03:04 STATE: test-backend-node-gpu.js start blazepose 
-2023-02-03 10:03:06 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:06 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:07 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:07 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose 
-2023-02-03 10:03:07 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} 
-2023-02-03 10:03:07 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 552 
-2023-02-03 10:03:07 STATE: test-backend-node-gpu.js passed: blazepose 
-2023-02-03 10:03:07 STATE: test-backend-node-gpu.js start efficientpose 
-2023-02-03 10:03:07 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:07 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:08 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:08 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose 
-2023-02-03 10:03:08 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} 
-2023-02-03 10:03:08 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 1113 
-2023-02-03 10:03:08 STATE: test-backend-node-gpu.js passed: efficientpose 
-2023-02-03 10:03:08 STATE: test-backend-node-gpu.js start posenet 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet 
-2023-02-03 10:03:09 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} 
-2023-02-03 10:03:09 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 136 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js passed: posenet 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js start movenet 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet 
-2023-02-03 10:03:09 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:09 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 114 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js passed: movenet 
-2023-02-03 10:03:09 INFO:  test-backend-node-gpu.js test face matching 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js passed: face database 40 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7829337456107771}} {"second":{"index":4,"similarity":0.5002921100080195}} {"third":{"index":4,"similarity":0.5402938601022594}} 
-2023-02-03 10:03:09 INFO:  test-backend-node-gpu.js test face similarity alternative 
-2023-02-03 10:03:09 STATE: test-backend-node-gpu.js start face embeddings 
-2023-02-03 10:03:11 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:03:11 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:11 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:11 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2023-02-03 10:03:11 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:11 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 497 
-2023-02-03 10:03:11 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} 
-2023-02-03 10:03:11 STATE: test-backend-node-gpu.js start face embeddings 
-2023-02-03 10:03:12 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:03:12 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:12 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:12 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2023-02-03 10:03:12 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:12 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 258 
-2023-02-03 10:03:12 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} 
-2023-02-03 10:03:12 INFO:  test-backend-node-gpu.js test face attention 
-2023-02-03 10:03:12 STATE: test-backend-node-gpu.js start face attention 
-2023-02-03 10:03:12 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:03:12 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention 
-2023-02-03 10:03:13 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:13 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 308 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js passed: face attention 
-2023-02-03 10:03:13 INFO:  test-backend-node-gpu.js test detectors 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js start detectors 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors 
-2023-02-03 10:03:13 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:13 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 193 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js passed: detector result face match 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js passed: detector result hand match 
-2023-02-03 10:03:13 INFO:  test-backend-node-gpu.js test: multi-instance 
-2023-02-03 10:03:13 STATE: test-backend-node-gpu.js start multi instance 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: detect: random multi instance 
-2023-02-03 10:03:14 DATA:  test-backend-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
-2023-02-03 10:03:14 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 77 
-2023-02-03 10:03:14 INFO:  test-backend-node-gpu.js test: first instance 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start multi instance 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2023-02-03 10:03:14 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:14 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 79 
-2023-02-03 10:03:14 INFO:  test-backend-node-gpu.js test: second instance 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start multi instance 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2023-02-03 10:03:14 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:14 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 74 
-2023-02-03 10:03:14 INFO:  test-backend-node-gpu.js test: concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js start concurrent 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 364 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 364 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 364 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 365 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 365 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 365 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 365 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 365 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 365 
-2023-02-03 10:03:15 INFO:  test-backend-node-gpu.js test: monkey-patch 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js event: image 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js event: detect 
-2023-02-03 10:03:15 STATE: test-backend-node-gpu.js passed: monkey patch 
-2023-02-03 10:03:16 STATE: test-backend-node-gpu.js passed: segmentation [262144] 
-2023-02-03 10:03:16 STATE: test-backend-node-gpu.js passeed: equal usage 
-2023-02-03 10:03:16 INFO:  test-backend-node-gpu.js test: input compare 
-2023-02-03 10:03:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2023-02-03 10:03:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2023-02-03 10:03:16 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 
-2023-02-03 10:03:16 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} 
-2023-02-03 10:03:16 INFO:  test-backend-node-gpu.js tensors 14855 
-2023-02-03 10:03:16 INFO:  test-backend-node-gpu.js test complete: 22335 ms 
-2023-02-03 10:03:17 INFO:  
-2023-02-03 10:03:17 INFO:  test-backend-node-wasm.js start 
-2023-02-03 10:03:17 DATA:  test-backend-node-wasm.js stdout: 2023-02-03 10:03:17 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.2.0/dist/ 
-2023-02-03 10:03:17 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ 
-2023-02-03 10:03:17 INFO:  test-backend-node-wasm.js test: configuration validation 
-2023-02-03 10:03:17 STATE: test-backend-node-wasm.js passed: configuration default validation [] 
-2023-02-03 10:03:17 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2023-02-03 10:03:17 INFO:  test-backend-node-wasm.js test: model load 
-2023-02-03 10:03:19 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"facemesh","loaded":true,"size":1477958,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"emotion","loaded":true,"size":820516,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"liveness","loaded":true,"size":592976,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"iris","loaded":true,"size":2599092,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"https://vladmandic.github.io/human-models/models/faceres.json"}] 
-2023-02-03 10:03:19 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} 
-2023-02-03 10:03:19 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
-2023-02-03 10:03:19 INFO:  test-backend-node-wasm.js test: warmup 
-2023-02-03 10:03:19 STATE: test-backend-node-wasm.js passed: create human 
-2023-02-03 10:03:19 INFO:  test-backend-node-wasm.js human version: 3.0.5 
-2023-02-03 10:03:19 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v19.1.0 
-2023-02-03 10:03:19 INFO:  test-backend-node-wasm.js tfjs version: 4.2.0 
-2023-02-03 10:03:19 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["wasm"],"initial":false,"tfjs":{"version":"4.2.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":128} 
-2023-02-03 10:03:19 STATE: test-backend-node-wasm.js passed: set backend: wasm 
-2023-02-03 10:03:19 STATE: test-backend-node-wasm.js tensors 1785 
-2023-02-03 10:03:19 STATE: test-backend-node-wasm.js  result: defined models: 11 loaded models: 11 
-2023-02-03 10:03:19 STATE: test-backend-node-wasm.js passed: load models 11 
-2023-02-03 10:03:19 STATE: test-backend-node-wasm.js passed: warmup: none default 
-2023-02-03 10:03:19 DATA:  test-backend-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2023-02-03 10:03:19 DATA:  test-backend-node-wasm.js  result: performance: load: null total: null 
-2023-02-03 10:03:19 STATE: test-backend-node-wasm.js passed: warmup none result match 
-2023-02-03 10:03:19 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js event: warmup 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js passed: warmup: face default 
-2023-02-03 10:03:20 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:20 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 536 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js passed: warmup face result match 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js event: warmup 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js passed: warmup: body default 
-2023-02-03 10:03:20 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:20 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 407 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js passed: warmup body result match 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2023-02-03 10:03:20 INFO:  test-backend-node-wasm.js test: details verification 
-2023-02-03 10:03:20 STATE: test-backend-node-wasm.js start default 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2023-02-03 10:03:21 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:21 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 346 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details face length 1 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details body length 1 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details hand length 1 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details gesture length 7 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details object length 1 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: details object 0.72 person 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:21 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2023-02-03 10:03:22 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
-2023-02-03 10:03:22 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:22 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:22 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2023-02-03 10:03:22 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:22 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:23 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:23 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2023-02-03 10:03:23 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:23 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:23 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:23 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2023-02-03 10:03:23 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2023-02-03 10:03:24 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:24 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:24 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2023-02-03 10:03:24 INFO:  test-backend-node-wasm.js test default 
-2023-02-03 10:03:24 STATE: test-backend-node-wasm.js start async 
-2023-02-03 10:03:26 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:26 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:26 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:26 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async 
-2023-02-03 10:03:26 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:26 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 419 
-2023-02-03 10:03:26 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 
-2023-02-03 10:03:26 INFO:  test-backend-node-wasm.js test sync 
-2023-02-03 10:03:26 STATE: test-backend-node-wasm.js start sync 
-2023-02-03 10:03:29 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:29 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:29 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:29 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync 
-2023-02-03 10:03:29 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:29 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 437 
-2023-02-03 10:03:29 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 
-2023-02-03 10:03:29 INFO:  test-backend-node-wasm.js test: image process 
-2023-02-03 10:03:29 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2023-02-03 10:03:29 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] 
-2023-02-03 10:03:29 INFO:  test-backend-node-wasm.js test: image null 
-2023-02-03 10:03:29 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor 
-2023-02-03 10:03:29 INFO:  test-backend-node-wasm.js test face similarity 
-2023-02-03 10:03:29 STATE: test-backend-node-wasm.js start face similarity 
-2023-02-03 10:03:31 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2023-02-03 10:03:31 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity 
-2023-02-03 10:03:32 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:32 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 442 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js start face similarity 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity 
-2023-02-03 10:03:32 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:32 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 329 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js start face similarity 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2023-02-03 10:03:32 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity 
-2023-02-03 10:03:33 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:33 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 314 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js passed: face descriptor 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.4472741837136387,0.5567931378245533],"descriptors":[1024,1024,1024]} 
-2023-02-03 10:03:33 INFO:  test-backend-node-wasm.js test object 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js start object 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object 
-2023-02-03 10:03:33 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:33 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 325 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js passed: centernet 
-2023-02-03 10:03:33 STATE: test-backend-node-wasm.js start object 
-2023-02-03 10:03:36 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"object","model":"nanodet","url":"https://vladmandic.github.io/human-models/models/nanodet.json","missing":["sparsetodense"],"backkend":"wasm"} 
-2023-02-03 10:03:36 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:36 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:36 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:36 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object 
-2023-02-03 10:03:36 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:36 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 207 
-2023-02-03 10:03:36 ERROR: test-backend-node-wasm.js failed: nanodet [] 
-2023-02-03 10:03:36 INFO:  test-backend-node-wasm.js test sensitive 
-2023-02-03 10:03:36 STATE: test-backend-node-wasm.js start sensitive 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive 
-2023-02-03 10:03:39 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:39 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 305 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js passed: sensitive result match 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js passed: sensitive face result match 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js passed: sensitive body result match 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js passed: sensitive hand result match 
-2023-02-03 10:03:39 INFO:  test-backend-node-wasm.js test body 
-2023-02-03 10:03:39 STATE: test-backend-node-wasm.js start blazepose 
-2023-02-03 10:03:45 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:45 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:45 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:45 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose 
-2023-02-03 10:03:45 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} 
-2023-02-03 10:03:45 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 484 
-2023-02-03 10:03:45 STATE: test-backend-node-wasm.js passed: blazepose 
-2023-02-03 10:03:45 STATE: test-backend-node-wasm.js start efficientpose 
-2023-02-03 10:03:46 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:46 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:47 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:47 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose 
-2023-02-03 10:03:47 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} 
-2023-02-03 10:03:47 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 739 
-2023-02-03 10:03:47 STATE: test-backend-node-wasm.js passed: efficientpose 
-2023-02-03 10:03:47 STATE: test-backend-node-wasm.js start posenet 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet 
-2023-02-03 10:03:48 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} 
-2023-02-03 10:03:48 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 279 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js passed: posenet 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js start movenet 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet 
-2023-02-03 10:03:48 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:48 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 230 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js passed: movenet 
-2023-02-03 10:03:48 INFO:  test-backend-node-wasm.js test face matching 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js passed: face database 40 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5002051178899982}} {"third":{"index":4,"similarity":0.5401586736511599}} 
-2023-02-03 10:03:48 INFO:  test-backend-node-wasm.js test face similarity alternative 
-2023-02-03 10:03:48 STATE: test-backend-node-wasm.js start face embeddings 
-2023-02-03 10:03:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2023-02-03 10:03:51 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:51 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:51 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2023-02-03 10:03:51 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:51 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 317 
-2023-02-03 10:03:51 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} 
-2023-02-03 10:03:51 STATE: test-backend-node-wasm.js start face embeddings 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2023-02-03 10:03:53 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:53 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 294 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} 
-2023-02-03 10:03:53 INFO:  test-backend-node-wasm.js test face attention 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js start face attention 
-2023-02-03 10:03:53 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"face attention","model":"facemesh","url":"https://vladmandic.github.io/human-models/models/facemesh-attention.json","missing":["atan2"],"backkend":"wasm"} 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face attention 
-2023-02-03 10:03:53 DATA:  test-backend-node-wasm.js  result: face: 0 body: 1 hand: 1 gesture: 2 object: 0 person: 0 {} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:53 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 129 
-2023-02-03 10:03:53 ERROR: test-backend-node-wasm.js failed: face attention {"annotations":0} 
-2023-02-03 10:03:53 INFO:  test-backend-node-wasm.js test detectors 
-2023-02-03 10:03:53 STATE: test-backend-node-wasm.js start detectors 
-2023-02-03 10:03:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:54 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg detectors 
-2023-02-03 10:03:55 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:55 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 150 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: detector result face match 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: detector result hand match 
-2023-02-03 10:03:55 INFO:  test-backend-node-wasm.js test: multi-instance 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start multi instance 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: detect: random multi instance 
-2023-02-03 10:03:55 DATA:  test-backend-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
-2023-02-03 10:03:55 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 116 
-2023-02-03 10:03:55 INFO:  test-backend-node-wasm.js test: first instance 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start multi instance 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2023-02-03 10:03:55 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:55 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 116 
-2023-02-03 10:03:55 INFO:  test-backend-node-wasm.js test: second instance 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start multi instance 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2023-02-03 10:03:55 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:55 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 123 
-2023-02-03 10:03:55 INFO:  test-backend-node-wasm.js test: concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js start concurrent 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2023-02-03 10:03:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2023-02-03 10:03:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2023-02-03 10:03:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2023-02-03 10:03:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2023-02-03 10:03:56 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:56 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:56 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 943 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 943 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 943 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 943 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 943 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 943 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 943 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 943 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2023-02-03 10:03:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 943 
-2023-02-03 10:03:57 INFO:  test-backend-node-wasm.js test: monkey-patch 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js event: image 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js event: detect 
-2023-02-03 10:03:57 STATE: test-backend-node-wasm.js passed: monkey patch 
-2023-02-03 10:03:58 STATE: test-backend-node-wasm.js passed: segmentation [262144] 
-2023-02-03 10:03:58 STATE: test-backend-node-wasm.js passeed: equal usage 
-2023-02-03 10:03:58 INFO:  test-backend-node-wasm.js test: input compare 
-2023-02-03 10:03:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2023-02-03 10:03:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2023-02-03 10:03:58 STATE: test-backend-node-wasm.js passed: image compare 0 23.280073018790848 
-2023-02-03 10:03:58 INFO:  test-backend-node-wasm.js events: {"image":29,"detect":29,"warmup":2} 
-2023-02-03 10:03:58 INFO:  test-backend-node-wasm.js tensors 14857 
-2023-02-03 10:03:58 INFO:  test-backend-node-wasm.js test complete: 41134 ms 
-2023-02-03 10:03:58 STATE: all tests complete 
-2023-02-03 10:03:58 INFO:    status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"../test/test-node-emotion.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"test-node-load.js","passed":1,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"test-node-gear.js","passed":3,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"test-backend-node.js","passed":125,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} 
-2023-02-03 10:03:58 INFO:    status {"test":"test-backend-node-wasm.js","passed":124,"failed":2} 
-2023-02-03 10:03:58 INFO:  failures {"count":2} 
-2023-02-03 10:03:58 WARN:    failed {"test":"test-backend-node-wasm.js","message":["error",["failed: nanodet",[]]]} 
-2023-02-03 10:03:58 WARN:    failed {"test":"test-backend-node-wasm.js","message":["error",["failed: face attention",{"annotations":0}]]} 
+2023-02-06 14:26:59 INFO:  @vladmandic/human version 3.0.5 
+2023-02-06 14:26:59 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 
+2023-02-06 14:26:59 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]},{"cmd":"../test/test-node-emotion.js","args":[]}] 
+2023-02-06 14:26:59 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start 
+2023-02-06 14:27:00 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start 
+2023-02-06 14:27:01 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start 
+2023-02-06 14:27:02 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start 
+2023-02-06 14:27:03 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start 
+2023-02-06 14:27:04 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start 
+2023-02-06 14:27:06 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start 
+2023-02-06 14:27:19 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start 
+2023-02-06 14:27:20 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start 
+2023-02-06 14:27:38 INFO:  {"cmd":"../test/test-node-emotion.js","args":[]} start 
+2023-02-06 14:27:44 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] 
+2023-02-06 14:27:44 INFO:  
+2023-02-06 14:27:44 INFO:  test-node-load.js start 
+2023-02-06 14:27:44 INFO:  test-node-load.js load start {"human":"3.0.5","tf":"4.2.0","progress":0} 
+2023-02-06 14:27:44 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} 
+2023-02-06 14:27:44 DATA:  test-node-load.js load interval {"elapsed":12,"progress":0} 
+2023-02-06 14:27:44 DATA:  test-node-load.js load interval {"elapsed":28,"progress":0.12865823126207815} 
+2023-02-06 14:27:44 DATA:  test-node-load.js load interval {"elapsed":39,"progress":0.2204080722078057} 
+2023-02-06 14:27:44 DATA:  test-node-load.js load interval {"elapsed":68,"progress":0.4726970548784964} 
+2023-02-06 14:27:44 DATA:  test-node-load.js load interval {"elapsed":83,"progress":0.68355493315821} 
+2023-02-06 14:27:44 STATE: test-node-load.js passed {"progress":1} 
+2023-02-06 14:27:44 INFO:  test-node-load.js load final {"progress":1} 
+2023-02-06 14:27:45 DATA:  test-node-load.js load interval {"elapsed":479,"progress":1} 
+2023-02-06 14:27:45 INFO:  
+2023-02-06 14:27:45 INFO:  test-node-gear.js start 
+2023-02-06 14:27:45 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] 
+2023-02-06 14:27:46 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg 
+2023-02-06 14:27:46 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92,"emotion":[]} 
+2023-02-06 14:27:46 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg 
+2023-02-06 14:27:46 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}],"emotion":[]} 
+2023-02-06 14:27:46 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg 
+2023-02-06 14:27:46 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99,"emotion":[]} 
+2023-02-06 14:27:46 INFO:  
+2023-02-06 14:27:46 INFO:  test-backend-node.js start 
+2023-02-06 14:27:46 INFO:  test-backend-node.js test: configuration validation 
+2023-02-06 14:27:46 STATE: test-backend-node.js passed: configuration default validation [] 
+2023-02-06 14:27:46 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2023-02-06 14:27:46 INFO:  test-backend-node.js test: model load 
+2023-02-06 14:27:46 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] 
+2023-02-06 14:27:46 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} 
+2023-02-06 14:27:46 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
+2023-02-06 14:27:46 INFO:  test-backend-node.js test: warmup 
+2023-02-06 14:27:46 STATE: test-backend-node.js passed: create human 
+2023-02-06 14:27:46 INFO:  test-backend-node.js human version: 3.0.5 
+2023-02-06 14:27:46 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v19.1.0 
+2023-02-06 14:27:46 INFO:  test-backend-node.js tfjs version: 4.2.0 
+2023-02-06 14:27:46 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.2.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} 
+2023-02-06 14:27:46 STATE: test-backend-node.js passed: set backend: tensorflow 
+2023-02-06 14:27:46 STATE: test-backend-node.js tensors 1785 
+2023-02-06 14:27:46 STATE: test-backend-node.js  result: defined models: 11 loaded models: 11 
+2023-02-06 14:27:46 STATE: test-backend-node.js passed: load models 11 
+2023-02-06 14:27:46 STATE: test-backend-node.js passed: warmup: none default 
+2023-02-06 14:27:46 DATA:  test-backend-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2023-02-06 14:27:46 DATA:  test-backend-node.js  result: performance: load: null total: null 
+2023-02-06 14:27:46 STATE: test-backend-node.js passed: warmup none result match 
+2023-02-06 14:27:46 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:47 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:47 STATE: test-backend-node.js event: warmup 
+2023-02-06 14:27:47 STATE: test-backend-node.js passed: warmup: face default 
+2023-02-06 14:27:47 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2023-02-06 14:27:47 DATA:  test-backend-node.js  result: performance: load: null total: 446 
+2023-02-06 14:27:47 STATE: test-backend-node.js passed: warmup face result match 
+2023-02-06 14:27:47 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:47 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:47 STATE: test-backend-node.js event: warmup 
+2023-02-06 14:27:47 STATE: test-backend-node.js passed: warmup: body default 
+2023-02-06 14:27:47 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:27:47 DATA:  test-backend-node.js  result: performance: load: null total: 353 
+2023-02-06 14:27:47 STATE: test-backend-node.js passed: warmup body result match 
+2023-02-06 14:27:47 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2023-02-06 14:27:47 INFO:  test-backend-node.js test: details verification 
+2023-02-06 14:27:47 STATE: test-backend-node.js start default 
+2023-02-06 14:27:47 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:47 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:48 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default 
+2023-02-06 14:27:48 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:27:48 DATA:  test-backend-node.js  result: performance: load: null total: 342 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details face length 1 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details face score 1 0.93 1 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details face arrays 4 478 1024 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details face liveness 0.83 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details body length 1 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details body 0.92 17 6 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details hand length 1 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details hand 0.51 0.73 point 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details hand arrays 21 5 7 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details gesture length 7 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details object length 1 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: details object 0.72 person 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2023-02-06 14:27:48 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:48 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2023-02-06 14:27:48 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2023-02-06 14:27:48 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:49 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:49 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2023-02-06 14:27:49 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:49 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:49 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:49 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2023-02-06 14:27:50 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:50 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:50 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:50 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2023-02-06 14:27:50 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2023-02-06 14:27:50 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:50 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:50 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2023-02-06 14:27:50 INFO:  test-backend-node.js test default 
+2023-02-06 14:27:50 STATE: test-backend-node.js start async 
+2023-02-06 14:27:51 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:51 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:51 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:51 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async 
+2023-02-06 14:27:51 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:27:51 DATA:  test-backend-node.js  result: performance: load: null total: 336 
+2023-02-06 14:27:51 STATE: test-backend-node.js passed: default result face match 1 female 0.97 
+2023-02-06 14:27:51 INFO:  test-backend-node.js test sync 
+2023-02-06 14:27:51 STATE: test-backend-node.js start sync 
+2023-02-06 14:27:51 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:51 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:52 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:52 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync 
+2023-02-06 14:27:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:27:52 DATA:  test-backend-node.js  result: performance: load: null total: 354 
+2023-02-06 14:27:52 STATE: test-backend-node.js passed: default sync 1 female 0.97 
+2023-02-06 14:27:52 INFO:  test-backend-node.js test: image process 
+2023-02-06 14:27:52 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:27:52 STATE: test-backend-node.js passed: image input null [1,256,256,3] 
+2023-02-06 14:27:52 INFO:  test-backend-node.js test: image null 
+2023-02-06 14:27:52 STATE: test-backend-node.js passed: invalid input could not convert input to tensor 
+2023-02-06 14:27:52 INFO:  test-backend-node.js test face similarity 
+2023-02-06 14:27:52 STATE: test-backend-node.js start face similarity 
+2023-02-06 14:27:52 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:27:52 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:52 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:52 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity 
+2023-02-06 14:27:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2023-02-06 14:27:52 DATA:  test-backend-node.js  result: performance: load: null total: 340 
+2023-02-06 14:27:52 STATE: test-backend-node.js start face similarity 
+2023-02-06 14:27:52 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:52 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:53 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:53 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity 
+2023-02-06 14:27:53 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:27:53 DATA:  test-backend-node.js  result: performance: load: null total: 332 
+2023-02-06 14:27:53 STATE: test-backend-node.js start face similarity 
+2023-02-06 14:27:53 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2023-02-06 14:27:53 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:53 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:53 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity 
+2023-02-06 14:27:53 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2023-02-06 14:27:53 DATA:  test-backend-node.js  result: performance: load: null total: 297 
+2023-02-06 14:27:53 STATE: test-backend-node.js passed: face descriptor 
+2023-02-06 14:27:53 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} 
+2023-02-06 14:27:53 INFO:  test-backend-node.js test object 
+2023-02-06 14:27:53 STATE: test-backend-node.js start object 
+2023-02-06 14:27:53 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:53 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:54 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:54 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object 
+2023-02-06 14:27:54 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:27:54 DATA:  test-backend-node.js  result: performance: load: null total: 323 
+2023-02-06 14:27:54 STATE: test-backend-node.js passed: centernet 
+2023-02-06 14:27:54 STATE: test-backend-node.js start object 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:55 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:55 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object 
+2023-02-06 14:27:55 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:27:55 DATA:  test-backend-node.js  result: performance: load: null total: 329 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: nanodet 
+2023-02-06 14:27:55 INFO:  test-backend-node.js test sensitive 
+2023-02-06 14:27:55 STATE: test-backend-node.js start sensitive 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:55 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:55 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive 
+2023-02-06 14:27:55 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:27:55 DATA:  test-backend-node.js  result: performance: load: null total: 288 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: sensitive result match 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: sensitive face result match 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: sensitive body result match 
+2023-02-06 14:27:55 STATE: test-backend-node.js passed: sensitive hand result match 
+2023-02-06 14:27:55 INFO:  test-backend-node.js test body 
+2023-02-06 14:27:55 STATE: test-backend-node.js start blazepose 
+2023-02-06 14:27:57 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:57 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:58 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:58 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose 
+2023-02-06 14:27:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} 
+2023-02-06 14:27:58 DATA:  test-backend-node.js  result: performance: load: null total: 411 
+2023-02-06 14:27:58 STATE: test-backend-node.js passed: blazepose 
+2023-02-06 14:27:58 STATE: test-backend-node.js start efficientpose 
+2023-02-06 14:27:59 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:27:59 STATE: test-backend-node.js event: image 
+2023-02-06 14:27:59 STATE: test-backend-node.js event: detect 
+2023-02-06 14:27:59 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose 
+2023-02-06 14:27:59 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} 
+2023-02-06 14:27:59 DATA:  test-backend-node.js  result: performance: load: null total: 350 
+2023-02-06 14:27:59 STATE: test-backend-node.js passed: efficientpose 
+2023-02-06 14:27:59 STATE: test-backend-node.js start posenet 
+2023-02-06 14:28:00 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:28:00 STATE: test-backend-node.js event: image 
+2023-02-06 14:28:00 STATE: test-backend-node.js event: detect 
+2023-02-06 14:28:00 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet 
+2023-02-06 14:28:00 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} 
+2023-02-06 14:28:00 DATA:  test-backend-node.js  result: performance: load: null total: 273 
+2023-02-06 14:28:00 STATE: test-backend-node.js passed: posenet 
+2023-02-06 14:28:00 STATE: test-backend-node.js start movenet 
+2023-02-06 14:28:00 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:28:00 STATE: test-backend-node.js event: image 
+2023-02-06 14:28:00 STATE: test-backend-node.js event: detect 
+2023-02-06 14:28:00 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet 
+2023-02-06 14:28:00 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:00 DATA:  test-backend-node.js  result: performance: load: null total: 281 
+2023-02-06 14:28:00 STATE: test-backend-node.js passed: movenet 
+2023-02-06 14:28:00 INFO:  test-backend-node.js test face matching 
+2023-02-06 14:28:00 STATE: test-backend-node.js passed: face database 40 
+2023-02-06 14:28:00 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} 
+2023-02-06 14:28:00 INFO:  test-backend-node.js test face similarity alternative 
+2023-02-06 14:28:00 STATE: test-backend-node.js start face embeddings 
+2023-02-06 14:28:01 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:01 STATE: test-backend-node.js event: image 
+2023-02-06 14:28:01 STATE: test-backend-node.js event: detect 
+2023-02-06 14:28:01 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings 
+2023-02-06 14:28:01 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:01 DATA:  test-backend-node.js  result: performance: load: null total: 325 
+2023-02-06 14:28:01 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} 
+2023-02-06 14:28:01 STATE: test-backend-node.js start face embeddings 
+2023-02-06 14:28:03 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:03 STATE: test-backend-node.js event: image 
+2023-02-06 14:28:03 STATE: test-backend-node.js event: detect 
+2023-02-06 14:28:03 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings 
+2023-02-06 14:28:03 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:03 DATA:  test-backend-node.js  result: performance: load: null total: 344 
+2023-02-06 14:28:03 STATE: test-backend-node.js passed: insightface {"embedding":512} 
+2023-02-06 14:28:03 INFO:  test-backend-node.js test face attention 
+2023-02-06 14:28:03 STATE: test-backend-node.js start face attention 
+2023-02-06 14:28:04 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:04 STATE: test-backend-node.js event: image 
+2023-02-06 14:28:04 STATE: test-backend-node.js event: detect 
+2023-02-06 14:28:04 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention 
+2023-02-06 14:28:04 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:04 DATA:  test-backend-node.js  result: performance: load: null total: 324 
+2023-02-06 14:28:04 STATE: test-backend-node.js passed: face attention 
+2023-02-06 14:28:04 INFO:  test-backend-node.js test detectors 
+2023-02-06 14:28:04 STATE: test-backend-node.js start detectors 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:28:05 STATE: test-backend-node.js event: image 
+2023-02-06 14:28:05 STATE: test-backend-node.js event: detect 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors 
+2023-02-06 14:28:05 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:05 DATA:  test-backend-node.js  result: performance: load: null total: 151 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: detector result face match 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: detector result hand match 
+2023-02-06 14:28:05 INFO:  test-backend-node.js test: multi-instance 
+2023-02-06 14:28:05 STATE: test-backend-node.js start multi instance 
+2023-02-06 14:28:05 STATE: test-backend-node.js event: image 
+2023-02-06 14:28:05 STATE: test-backend-node.js event: detect 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: detect: random multi instance 
+2023-02-06 14:28:05 DATA:  test-backend-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
+2023-02-06 14:28:05 DATA:  test-backend-node.js  result: performance: load: null total: 139 
+2023-02-06 14:28:05 INFO:  test-backend-node.js test: first instance 
+2023-02-06 14:28:05 STATE: test-backend-node.js start multi instance 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2023-02-06 14:28:05 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:28:05 DATA:  test-backend-node.js  result: performance: load: null total: 143 
+2023-02-06 14:28:05 INFO:  test-backend-node.js test: second instance 
+2023-02-06 14:28:05 STATE: test-backend-node.js start multi instance 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2023-02-06 14:28:05 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:28:05 DATA:  test-backend-node.js  result: performance: load: null total: 135 
+2023-02-06 14:28:05 INFO:  test-backend-node.js test: concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js start concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js start concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js start concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js start concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js start concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js start concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js start concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js start concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js start concurrent 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:05 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:06 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:28:06 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:28:06 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:28:06 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2023-02-06 14:28:06 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2023-02-06 14:28:06 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
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+2023-02-06 14:28:07 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: performance: load: null total: 1208 
+2023-02-06 14:28:07 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: performance: load: null total: 1208 
+2023-02-06 14:28:07 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: performance: load: null total: 1208 
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+2023-02-06 14:28:07 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: performance: load: null total: 1208 
+2023-02-06 14:28:07 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: performance: load: null total: 1208 
+2023-02-06 14:28:07 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: performance: load: null total: 1208 
+2023-02-06 14:28:07 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: performance: load: null total: 1208 
+2023-02-06 14:28:07 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: performance: load: null total: 1208 
+2023-02-06 14:28:07 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:07 DATA:  test-backend-node.js  result: performance: load: null total: 1208 
+2023-02-06 14:28:07 INFO:  test-backend-node.js test: monkey-patch 
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+2023-02-06 14:28:08 STATE: test-backend-node.js event: detect 
+2023-02-06 14:28:08 STATE: test-backend-node.js passed: monkey patch 
+2023-02-06 14:28:08 STATE: test-backend-node.js passed: segmentation [262144] 
+2023-02-06 14:28:08 STATE: test-backend-node.js passeed: equal usage 
+2023-02-06 14:28:08 INFO:  test-backend-node.js test: input compare 
+2023-02-06 14:28:08 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:08 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2023-02-06 14:28:08 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 
+2023-02-06 14:28:08 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} 
+2023-02-06 14:28:08 INFO:  test-backend-node.js tensors 14855 
+2023-02-06 14:28:08 INFO:  test-backend-node.js test complete: 22364 ms 
+2023-02-06 14:28:09 INFO:  
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js start 
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js test: configuration validation 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js passed: configuration default validation [] 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js test: model load 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] 
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} 
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js test: warmup 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js passed: create human 
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js human version: 3.0.5 
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v19.1.0 
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js tfjs version: 4.2.0 
+2023-02-06 14:28:09 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.2.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":true},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js passed: set backend: tensorflow 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js tensors 1785 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js  result: defined models: 11 loaded models: 11 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js passed: load models 11 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js passed: warmup: none default 
+2023-02-06 14:28:09 DATA:  test-backend-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2023-02-06 14:28:09 DATA:  test-backend-node-gpu.js  result: performance: load: null total: null 
+2023-02-06 14:28:09 STATE: test-backend-node-gpu.js passed: warmup none result match 
+2023-02-06 14:28:10 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js event: warmup 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: warmup: face default 
+2023-02-06 14:28:12 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2023-02-06 14:28:12 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 1858 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: warmup face result match 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js event: warmup 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: warmup: body default 
+2023-02-06 14:28:12 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:12 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 172 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: warmup body result match 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2023-02-06 14:28:12 INFO:  test-backend-node-gpu.js test: details verification 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js start default 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2023-02-06 14:28:12 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:12 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 160 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details face length 1 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details body length 1 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details hand length 1 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details gesture length 7 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details object length 1 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: details object 0.72 person 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2023-02-06 14:28:12 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
+2023-02-06 14:28:13 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:14 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:14 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2023-02-06 14:28:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2023-02-06 14:28:14 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:14 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:14 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2023-02-06 14:28:14 INFO:  test-backend-node-gpu.js test default 
+2023-02-06 14:28:14 STATE: test-backend-node-gpu.js start async 
+2023-02-06 14:28:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2023-02-06 14:28:14 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:15 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async 
+2023-02-06 14:28:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 422 
+2023-02-06 14:28:15 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 
+2023-02-06 14:28:15 INFO:  test-backend-node-gpu.js test sync 
+2023-02-06 14:28:15 STATE: test-backend-node-gpu.js start sync 
+2023-02-06 14:28:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2023-02-06 14:28:15 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync 
+2023-02-06 14:28:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 390 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 
+2023-02-06 14:28:16 INFO:  test-backend-node-gpu.js test: image process 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] 
+2023-02-06 14:28:16 INFO:  test-backend-node-gpu.js test: image null 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor 
+2023-02-06 14:28:16 INFO:  test-backend-node-gpu.js test face similarity 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js start face similarity 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity 
+2023-02-06 14:28:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 428 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js start face similarity 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js event: image 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js event: detect 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity 
+2023-02-06 14:28:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 162 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js start face similarity 
+2023-02-06 14:28:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
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+2023-02-06 14:28:17 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity 
+2023-02-06 14:28:17 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2023-02-06 14:28:17 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 166 
+2023-02-06 14:28:17 STATE: test-backend-node-gpu.js passed: face descriptor 
+2023-02-06 14:28:17 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.4475006597943763,0.5570889682705169],"descriptors":[1024,1024,1024]} 
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+2023-02-06 14:28:17 STATE: test-backend-node-gpu.js start object 
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+2023-02-06 14:28:17 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object 
+2023-02-06 14:28:17 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:17 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 165 
+2023-02-06 14:28:17 STATE: test-backend-node-gpu.js passed: centernet 
+2023-02-06 14:28:17 STATE: test-backend-node-gpu.js start object 
+2023-02-06 14:28:18 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2023-02-06 14:28:19 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object 
+2023-02-06 14:28:19 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:19 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 524 
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+2023-02-06 14:28:19 INFO:  test-backend-node-gpu.js test sensitive 
+2023-02-06 14:28:19 STATE: test-backend-node-gpu.js start sensitive 
+2023-02-06 14:28:19 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2023-02-06 14:28:19 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:19 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 312 
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+2023-02-06 14:28:19 STATE: test-backend-node-gpu.js passed: sensitive face result match 
+2023-02-06 14:28:19 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
+2023-02-06 14:28:19 STATE: test-backend-node-gpu.js passed: sensitive body result match 
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+2023-02-06 14:28:19 INFO:  test-backend-node-gpu.js test body 
+2023-02-06 14:28:19 STATE: test-backend-node-gpu.js start blazepose 
+2023-02-06 14:28:21 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2023-02-06 14:28:22 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose 
+2023-02-06 14:28:22 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} 
+2023-02-06 14:28:22 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 517 
+2023-02-06 14:28:22 STATE: test-backend-node-gpu.js passed: blazepose 
+2023-02-06 14:28:22 STATE: test-backend-node-gpu.js start efficientpose 
+2023-02-06 14:28:23 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2023-02-06 14:28:23 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose 
+2023-02-06 14:28:23 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} 
+2023-02-06 14:28:23 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 954 
+2023-02-06 14:28:23 STATE: test-backend-node-gpu.js passed: efficientpose 
+2023-02-06 14:28:23 STATE: test-backend-node-gpu.js start posenet 
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+2023-02-06 14:28:24 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet 
+2023-02-06 14:28:24 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} 
+2023-02-06 14:28:24 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 145 
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+2023-02-06 14:28:24 STATE: test-backend-node-gpu.js start movenet 
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+2023-02-06 14:28:25 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet 
+2023-02-06 14:28:25 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:25 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 110 
+2023-02-06 14:28:25 STATE: test-backend-node-gpu.js passed: movenet 
+2023-02-06 14:28:25 INFO:  test-backend-node-gpu.js test face matching 
+2023-02-06 14:28:25 STATE: test-backend-node-gpu.js passed: face database 40 
+2023-02-06 14:28:25 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7829337456107771}} {"second":{"index":4,"similarity":0.5002921100080195}} {"third":{"index":4,"similarity":0.5402938601022594}} 
+2023-02-06 14:28:25 INFO:  test-backend-node-gpu.js test face similarity alternative 
+2023-02-06 14:28:25 STATE: test-backend-node-gpu.js start face embeddings 
+2023-02-06 14:28:25 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
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+2023-02-06 14:28:26 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings 
+2023-02-06 14:28:26 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:26 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 396 
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+2023-02-06 14:28:26 STATE: test-backend-node-gpu.js start face embeddings 
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+2023-02-06 14:28:27 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:27 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 340 
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+2023-02-06 14:28:27 STATE: test-backend-node-gpu.js start face attention 
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+2023-02-06 14:28:27 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention 
+2023-02-06 14:28:27 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:27 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 265 
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+2023-02-06 14:28:27 STATE: test-backend-node-gpu.js start detectors 
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+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors 
+2023-02-06 14:28:28 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
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+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js passed: detect: random multi instance 
+2023-02-06 14:28:28 DATA:  test-backend-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
+2023-02-06 14:28:28 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 41 
+2023-02-06 14:28:28 INFO:  test-backend-node-gpu.js test: first instance 
+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js start multi instance 
+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2023-02-06 14:28:28 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:28:28 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 50 
+2023-02-06 14:28:28 INFO:  test-backend-node-gpu.js test: second instance 
+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js start multi instance 
+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2023-02-06 14:28:28 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:28:28 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 42 
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+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js start concurrent 
+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
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+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2023-02-06 14:28:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2023-02-06 14:28:29 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2023-02-06 14:28:29 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2023-02-06 14:28:29 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
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+2023-02-06 14:28:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
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+2023-02-06 14:28:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
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+2023-02-06 14:28:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
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+2023-02-06 14:28:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
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+2023-02-06 14:28:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
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+2023-02-06 14:28:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:29 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 401 
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+2023-02-06 14:28:30 STATE: test-backend-node-gpu.js passed: monkey patch 
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+2023-02-06 14:28:30 STATE: test-backend-node-gpu.js passeed: equal usage 
+2023-02-06 14:28:30 INFO:  test-backend-node-gpu.js test: input compare 
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+2023-02-06 14:28:30 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 
+2023-02-06 14:28:30 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} 
+2023-02-06 14:28:30 INFO:  test-backend-node-gpu.js tensors 14855 
+2023-02-06 14:28:30 INFO:  test-backend-node-gpu.js test complete: 21179 ms 
+2023-02-06 14:28:31 INFO:  
+2023-02-06 14:28:31 INFO:  test-backend-node-wasm.js start 
+2023-02-06 14:28:31 DATA:  test-backend-node-wasm.js stdout: 2023-02-06 14:28:31 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.2.0/dist/ 
+2023-02-06 14:28:31 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ 
+2023-02-06 14:28:31 INFO:  test-backend-node-wasm.js test: configuration validation 
+2023-02-06 14:28:31 STATE: test-backend-node-wasm.js passed: configuration default validation [] 
+2023-02-06 14:28:31 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2023-02-06 14:28:31 INFO:  test-backend-node-wasm.js test: model load 
+2023-02-06 14:28:33 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"liveness","loaded":true,"size":592976,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"iris","loaded":true,"size":2599092,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"https://vladmandic.github.io/human-models/models/faceres.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"https://vladmandic.github.io/human-models/models/centernet.json"}] 
+2023-02-06 14:28:33 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} 
+2023-02-06 14:28:33 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
+2023-02-06 14:28:33 INFO:  test-backend-node-wasm.js test: warmup 
+2023-02-06 14:28:33 STATE: test-backend-node-wasm.js passed: create human 
+2023-02-06 14:28:33 INFO:  test-backend-node-wasm.js human version: 3.0.5 
+2023-02-06 14:28:33 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v19.1.0 
+2023-02-06 14:28:33 INFO:  test-backend-node-wasm.js tfjs version: 4.2.0 
+2023-02-06 14:28:33 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["wasm"],"initial":false,"tfjs":{"version":"4.2.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":128} 
+2023-02-06 14:28:33 STATE: test-backend-node-wasm.js passed: set backend: wasm 
+2023-02-06 14:28:33 STATE: test-backend-node-wasm.js tensors 1785 
+2023-02-06 14:28:33 STATE: test-backend-node-wasm.js  result: defined models: 11 loaded models: 11 
+2023-02-06 14:28:33 STATE: test-backend-node-wasm.js passed: load models 11 
+2023-02-06 14:28:33 STATE: test-backend-node-wasm.js passed: warmup: none default 
+2023-02-06 14:28:33 DATA:  test-backend-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2023-02-06 14:28:33 DATA:  test-backend-node-wasm.js  result: performance: load: null total: null 
+2023-02-06 14:28:33 STATE: test-backend-node-wasm.js passed: warmup none result match 
+2023-02-06 14:28:33 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js event: warmup 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js passed: warmup: face default 
+2023-02-06 14:28:34 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:34 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 556 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js passed: warmup face result match 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js event: warmup 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js passed: warmup: body default 
+2023-02-06 14:28:34 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:34 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 357 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js passed: warmup body result match 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2023-02-06 14:28:34 INFO:  test-backend-node-wasm.js test: details verification 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js start default 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:34 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2023-02-06 14:28:35 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:35 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 358 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details face length 1 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details body length 1 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details hand length 1 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details gesture length 7 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details object length 1 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: details object 0.72 person 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:35 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2023-02-06 14:28:36 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
+2023-02-06 14:28:36 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:36 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:36 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2023-02-06 14:28:36 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:36 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:37 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:37 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2023-02-06 14:28:37 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:37 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:37 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:37 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2023-02-06 14:28:37 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2023-02-06 14:28:37 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:38 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:38 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2023-02-06 14:28:38 INFO:  test-backend-node-wasm.js test default 
+2023-02-06 14:28:38 STATE: test-backend-node-wasm.js start async 
+2023-02-06 14:28:40 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:40 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:40 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:40 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async 
+2023-02-06 14:28:40 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:40 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 422 
+2023-02-06 14:28:40 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 
+2023-02-06 14:28:40 INFO:  test-backend-node-wasm.js test sync 
+2023-02-06 14:28:40 STATE: test-backend-node-wasm.js start sync 
+2023-02-06 14:28:42 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:42 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:42 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:42 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync 
+2023-02-06 14:28:42 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:42 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 438 
+2023-02-06 14:28:42 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 
+2023-02-06 14:28:42 INFO:  test-backend-node-wasm.js test: image process 
+2023-02-06 14:28:42 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2023-02-06 14:28:42 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] 
+2023-02-06 14:28:42 INFO:  test-backend-node-wasm.js test: image null 
+2023-02-06 14:28:42 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor 
+2023-02-06 14:28:42 INFO:  test-backend-node-wasm.js test face similarity 
+2023-02-06 14:28:42 STATE: test-backend-node-wasm.js start face similarity 
+2023-02-06 14:28:44 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2023-02-06 14:28:44 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:44 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:44 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity 
+2023-02-06 14:28:44 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2023-02-06 14:28:44 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 456 
+2023-02-06 14:28:44 STATE: test-backend-node-wasm.js start face similarity 
+2023-02-06 14:28:44 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:44 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity 
+2023-02-06 14:28:45 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:45 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 318 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js start face similarity 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity 
+2023-02-06 14:28:45 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2023-02-06 14:28:45 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 315 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js passed: face descriptor 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.4472741837136387,0.5567931378245533],"descriptors":[1024,1024,1024]} 
+2023-02-06 14:28:45 INFO:  test-backend-node-wasm.js test object 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js start object 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:45 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:46 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:46 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object 
+2023-02-06 14:28:46 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:46 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 325 
+2023-02-06 14:28:46 STATE: test-backend-node-wasm.js passed: centernet 
+2023-02-06 14:28:46 STATE: test-backend-node-wasm.js start object 
+2023-02-06 14:28:47 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"object","model":"nanodet","url":"https://vladmandic.github.io/human-models/models/nanodet.json","missing":["sparsetodense"],"backkend":"wasm"} 
+2023-02-06 14:28:47 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:47 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:47 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:47 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object 
+2023-02-06 14:28:47 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:47 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 210 
+2023-02-06 14:28:47 ERROR: test-backend-node-wasm.js failed: nanodet [] 
+2023-02-06 14:28:47 INFO:  test-backend-node-wasm.js test sensitive 
+2023-02-06 14:28:47 STATE: test-backend-node-wasm.js start sensitive 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive 
+2023-02-06 14:28:49 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:49 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 310 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js passed: sensitive result match 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js passed: sensitive face result match 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js passed: sensitive body result match 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js passed: sensitive hand result match 
+2023-02-06 14:28:49 INFO:  test-backend-node-wasm.js test body 
+2023-02-06 14:28:49 STATE: test-backend-node-wasm.js start blazepose 
+2023-02-06 14:28:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:53 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:53 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose 
+2023-02-06 14:28:53 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} 
+2023-02-06 14:28:53 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 483 
+2023-02-06 14:28:53 STATE: test-backend-node-wasm.js passed: blazepose 
+2023-02-06 14:28:53 STATE: test-backend-node-wasm.js start efficientpose 
+2023-02-06 14:28:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:55 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:55 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:55 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose 
+2023-02-06 14:28:55 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} 
+2023-02-06 14:28:55 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 682 
+2023-02-06 14:28:55 STATE: test-backend-node-wasm.js passed: efficientpose 
+2023-02-06 14:28:55 STATE: test-backend-node-wasm.js start posenet 
+2023-02-06 14:28:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:56 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:56 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:56 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet 
+2023-02-06 14:28:56 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} 
+2023-02-06 14:28:56 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 280 
+2023-02-06 14:28:56 STATE: test-backend-node-wasm.js passed: posenet 
+2023-02-06 14:28:56 STATE: test-backend-node-wasm.js start movenet 
+2023-02-06 14:28:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:28:57 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:28:57 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:28:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet 
+2023-02-06 14:28:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:28:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 224 
+2023-02-06 14:28:57 STATE: test-backend-node-wasm.js passed: movenet 
+2023-02-06 14:28:57 INFO:  test-backend-node-wasm.js test face matching 
+2023-02-06 14:28:57 STATE: test-backend-node-wasm.js passed: face database 40 
+2023-02-06 14:28:57 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5002051178899982}} {"third":{"index":4,"similarity":0.5401586736511599}} 
+2023-02-06 14:28:57 INFO:  test-backend-node-wasm.js test face similarity alternative 
+2023-02-06 14:28:57 STATE: test-backend-node-wasm.js start face embeddings 
+2023-02-06 14:29:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2023-02-06 14:29:00 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:29:00 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:29:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings 
+2023-02-06 14:29:00 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:29:00 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 323 
+2023-02-06 14:29:00 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} 
+2023-02-06 14:29:00 STATE: test-backend-node-wasm.js start face embeddings 
+2023-02-06 14:29:01 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2023-02-06 14:29:01 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:29:02 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:29:02 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings 
+2023-02-06 14:29:02 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:29:02 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 285 
+2023-02-06 14:29:02 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} 
+2023-02-06 14:29:02 INFO:  test-backend-node-wasm.js test face attention 
+2023-02-06 14:29:02 STATE: test-backend-node-wasm.js start face attention 
+2023-02-06 14:29:02 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"face attention","model":"facemesh","url":"https://vladmandic.github.io/human-models/models/facemesh-attention.json","missing":["atan2"],"backkend":"wasm"} 
+2023-02-06 14:29:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2023-02-06 14:29:02 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:29:02 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:29:02 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face attention 
+2023-02-06 14:29:02 DATA:  test-backend-node-wasm.js  result: face: 0 body: 1 hand: 1 gesture: 2 object: 0 person: 0 {} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:29:02 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 118 
+2023-02-06 14:29:02 ERROR: test-backend-node-wasm.js failed: face attention {"annotations":0} 
+2023-02-06 14:29:02 INFO:  test-backend-node-wasm.js test detectors 
+2023-02-06 14:29:02 STATE: test-backend-node-wasm.js start detectors 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg detectors 
+2023-02-06 14:29:03 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:29:03 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 145 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js passed: detector result face match 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js passed: detector result hand match 
+2023-02-06 14:29:03 INFO:  test-backend-node-wasm.js test: multi-instance 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js start multi instance 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js passed: detect: random multi instance 
+2023-02-06 14:29:03 DATA:  test-backend-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
+2023-02-06 14:29:03 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 104 
+2023-02-06 14:29:03 INFO:  test-backend-node-wasm.js test: first instance 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js start multi instance 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2023-02-06 14:29:03 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:29:03 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 111 
+2023-02-06 14:29:03 INFO:  test-backend-node-wasm.js test: second instance 
+2023-02-06 14:29:03 STATE: test-backend-node-wasm.js start multi instance 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2023-02-06 14:29:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:29:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 120 
+2023-02-06 14:29:04 INFO:  test-backend-node-wasm.js test: concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js start concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js start concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js start concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js start concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js start concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js start concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js start concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js start concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js start concurrent 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:29:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:29:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:29:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2023-02-06 14:29:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2023-02-06 14:29:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2023-02-06 14:29:05 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:29:05 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:29:05 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 958 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 958 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 958 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 958 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 958 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 958 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 958 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 958 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2023-02-06 14:29:06 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 958 
+2023-02-06 14:29:06 INFO:  test-backend-node-wasm.js test: monkey-patch 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js event: image 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js event: detect 
+2023-02-06 14:29:06 STATE: test-backend-node-wasm.js passed: monkey patch 
+2023-02-06 14:29:07 STATE: test-backend-node-wasm.js passed: segmentation [262144] 
+2023-02-06 14:29:07 STATE: test-backend-node-wasm.js passeed: equal usage 
+2023-02-06 14:29:07 INFO:  test-backend-node-wasm.js test: input compare 
+2023-02-06 14:29:07 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2023-02-06 14:29:07 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2023-02-06 14:29:07 STATE: test-backend-node-wasm.js passed: image compare 0 23.280073018790848 
+2023-02-06 14:29:07 INFO:  test-backend-node-wasm.js events: {"image":29,"detect":29,"warmup":2} 
+2023-02-06 14:29:07 INFO:  test-backend-node-wasm.js tensors 14857 
+2023-02-06 14:29:07 INFO:  test-backend-node-wasm.js test complete: 35649 ms 
+2023-02-06 14:29:07 STATE: all tests complete 
+2023-02-06 14:29:07 INFO:    status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"../test/test-node-emotion.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"test-node-load.js","passed":1,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"test-node-gear.js","passed":3,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"test-backend-node.js","passed":125,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} 
+2023-02-06 14:29:07 INFO:    status {"test":"test-backend-node-wasm.js","passed":124,"failed":2} 
+2023-02-06 14:29:07 INFO:  failures {"count":2} 
+2023-02-06 14:29:07 WARN:    failed {"test":"test-backend-node-wasm.js","message":["error",["failed: nanodet",[]]]} 
+2023-02-06 14:29:07 WARN:    failed {"test":"test-backend-node-wasm.js","message":["error",["failed: face attention",{"annotations":0}]]} 
diff --git a/typedoc/assets/search.js b/typedoc/assets/search.js
index 52c31a5f..5287107d 100644
--- a/typedoc/assets/search.js
+++ b/typedoc/assets/search.js
@@ -1 +1 @@
-window.searchData = JSON.parse("{\"kinds\":{\"4\":\"Namespace\",\"8\":\"Enumeration\",\"16\":\"Enumeration Member\",\"32\":\"Variable\",\"64\":\"Function\",\"128\":\"Class\",\"256\":\"Interface\",\"512\":\"Constructor\",\"1024\":\"Property\",\"2048\":\"Method\",\"65536\":\"Type literal\",\"262144\":\"Accessor\",\"4194304\":\"Type alias\",\"8388608\":\"Reference\"},\"rows\":[{\"kind\":128,\"name\":\"Human\",\"url\":\"classes/Human.html\",\"classes\":\"tsd-kind-class\"},{\"kind\":512,\"name\":\"constructor\",\"url\":\"classes/Human.html#constructor\",\"classes\":\"tsd-kind-constructor tsd-parent-kind-class\",\"parent\":\"Human\"},{\"kind\":1024,\"name\":\"version\",\"url\":\"classes/Human.html#version\",\"classes\":\"tsd-kind-property tsd-parent-kind-class\",\"parent\":\"Human\"},{\"kind\":1024,\"name\":\"config\",\"url\":\"classes/Human.html#config\",\"classes\":\"tsd-kind-property tsd-parent-kind-class\",\"parent\":\"Human\"},{\"kind\":1024,\"name\":\"result\",\"url\":\"classes/Human.html#result\",\"classes\":\"tsd-kind-property tsd-parent-kind-class\",\"parent\":\"Human\"},{\"kind\":1024,\"name\":\"state\",\"url\":\"classes/Human.html#state\",\"classes\":\"tsd-kind-property tsd-parent-kind-class\",\"parent\":\"Human\"},{\"kind\":1024,\"name\":\"process\",\"url\":\"classes/Human.html#process\",\"classes\":\"tsd-kind-property tsd-parent-kind-class\",\"parent\":\"Human\"},{\"kind\":65536,\"name\":\"__type\",\"url\":\"classes/Human.html#process.__type\",\"classes\":\"tsd-kind-type-literal tsd-parent-kind-property\",\"parent\":\"Human.process\"},{\"kind\":1024,\"name\":\"tensor\",\"url\":\"classes/Human.html#process.__type.tensor\",\"classes\":\"tsd-kind-property tsd-parent-kind-type-literal\",\"parent\":\"Human.process.__type\"},{\"kind\":1024,\"name\":\"canvas\",\"url\":\"classes/Human.html#process.__type.canvas\",\"classes\":\"tsd-kind-property 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 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
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+<li class="tsd-signature tsd-anchor-link" id="as2D.as2D-1">as2D<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>rows<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, columns<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="../types/Tensor2D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor2D</a><a href="#as2D.as2D-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -682,13 +682,13 @@ synchronously.</p>
 <h5>rows: <span class="tsd-signature-type">number</span></h5></li>
 <li>
 <h5>columns: <span class="tsd-signature-type">number</span></h5></li></ul></div>
-<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Tensor2D</span></h4><aside class="tsd-sources">
+<h4 class="tsd-returns-title">Returns <a href="../types/Tensor2D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor2D</a></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.d.ts:21</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="as3D" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>as3D</span><a href="#as3D" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="as3D.as3D-1">as3D<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>rows<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, columns<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, depth<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">Tensor3D</span><a href="#as3D.as3D-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="as3D.as3D-1">as3D<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>rows<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, columns<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, depth<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="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a><a href="#as3D.as3D-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -704,7 +704,7 @@ synchronously.</p>
 <h5>columns: <span class="tsd-signature-type">number</span></h5></li>
 <li>
 <h5>depth: <span class="tsd-signature-type">number</span></h5></li></ul></div>
-<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Tensor3D</span></h4><aside class="tsd-sources">
+<h4 class="tsd-returns-title">Returns <a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.d.ts:21</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="as4D" class="tsd-anchor"></a>
@@ -847,7 +847,7 @@ synchronously.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="atan2" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>atan2</span><a href="#atan2" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="atan2.atan2-1">atan2<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#atan2.atan2-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="atan2.atan2-1">atan2<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#atan2.atan2-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -858,7 +858,7 @@ synchronously.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.d.ts:5</li></ul></aside></li></ul></section>
@@ -889,7 +889,7 @@ synchronously.</p>
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
@@ -907,7 +907,7 @@ synchronously.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="batchNorm" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>batch<wbr/>Norm</span><a href="#batchNorm" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="batchNorm.batchNorm-1">batch<wbr/>Norm<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>mean<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor1D</span>, variance<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor1D</span>, offset<span class="tsd-signature-symbol">?: </span><a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor1D</span>, scale<span class="tsd-signature-symbol">?: </span><a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor1D</span>, varianceEpsilon<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-1.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><a href="#batchNorm.batchNorm-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="batchNorm.batchNorm-1">batch<wbr/>Norm<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>mean<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a>, variance<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a>, offset<span class="tsd-signature-symbol">?: </span><a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a>, scale<span class="tsd-signature-symbol">?: </span><a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a>, varianceEpsilon<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-1.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><a href="#batchNorm.batchNorm-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -918,13 +918,13 @@ synchronously.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>mean: <a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor1D</span></h5></li>
+<h5>mean: <a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a></h5></li>
 <li>
-<h5>variance: <a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor1D</span></h5></li>
+<h5>variance: <a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a></h5></li>
 <li>
-<h5><code class="tsd-tag ts-flagOptional">Optional</code> offset: <a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor1D</span></h5></li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> offset: <a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a></h5></li>
 <li>
-<h5><code class="tsd-tag ts-flagOptional">Optional</code> scale: <a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor1D</span></h5></li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> scale: <a href="Tensor-1.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><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a></h5></li>
 <li>
 <h5><code class="tsd-tag ts-flagOptional">Optional</code> varianceEpsilon: <span class="tsd-signature-type">number</span></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <a href="Tensor-1.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><aside class="tsd-sources">
@@ -1090,7 +1090,7 @@ synchronously.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="concat" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>concat</span><a href="#concat" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="concat.concat-1">concat<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>tensors<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">TensorLike</span><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>, axis<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" data-tsd-kind="Type parameter">T</span><a href="#concat.concat-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="concat.concat-1">concat<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>tensors<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><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><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>, axis<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" data-tsd-kind="Type parameter">T</span><a href="#concat.concat-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1101,7 +1101,7 @@ synchronously.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>tensors: <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">TensorLike</span><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></h5></li>
+<h5>tensors: <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">(</span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><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></h5></li>
 <li>
 <h5><code class="tsd-tag ts-flagOptional">Optional</code> axis: <span class="tsd-signature-type">number</span></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
@@ -1110,18 +1110,18 @@ synchronously.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="conv1d" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>conv1d</span><a href="#conv1d" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="conv1d.conv1d-1">conv1d<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>filter<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Tensor3D</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike3D</span>, stride<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, pad<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;valid&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;same&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">ExplicitPadding</span>, dataFormat<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">&quot;NWC&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;NCW&quot;</span>, dilation<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">number</span>, dimRoundingMode<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">&quot;round&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;floor&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;ceil&quot;</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><a href="#conv1d.conv1d-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="conv1d.conv1d-1">conv1d<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>filter<span class="tsd-signature-symbol">: </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike3D</span>, stride<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, pad<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;valid&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;same&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">ExplicitPadding</span>, dataFormat<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">&quot;NWC&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;NCW&quot;</span>, dilation<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">number</span>, dimRoundingMode<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">&quot;round&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;floor&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;ceil&quot;</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><a href="#conv1d.conv1d-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-type">Tensor3D</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor2D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor2D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor2D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>filter: <span class="tsd-signature-type">Tensor3D</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike3D</span></h5></li>
+<h5>filter: <a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike3D</span></h5></li>
 <li>
 <h5>stride: <span class="tsd-signature-type">number</span></h5></li>
 <li>
@@ -1144,7 +1144,7 @@ synchronously.</p>
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
@@ -1172,7 +1172,7 @@ synchronously.</p>
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
@@ -1363,7 +1363,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
@@ -1385,18 +1385,18 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dilation2d" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>dilation2d</span><a href="#dilation2d" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="dilation2d.dilation2d-1">dilation2d<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>filter<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Tensor3D</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike3D</span>, strides<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">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span>, pad<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">&quot;valid&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;same&quot;</span>, dilations<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">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span>, dataFormat<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">&quot;NHWC&quot;</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><a href="#dilation2d.dilation2d-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="dilation2d.dilation2d-1">dilation2d<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>filter<span class="tsd-signature-symbol">: </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike3D</span>, strides<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">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span>, pad<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">&quot;valid&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;same&quot;</span>, dilations<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">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span>, dataFormat<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">&quot;NHWC&quot;</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><a href="#dilation2d.dilation2d-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>filter: <span class="tsd-signature-type">Tensor3D</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike3D</span></h5></li>
+<h5>filter: <a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike3D</span></h5></li>
 <li>
 <h5>strides: <span class="tsd-signature-type">number</span><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></h5></li>
 <li>
@@ -1422,7 +1422,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="div" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>div</span><a href="#div" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="div.div-1">div<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#div.div-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="div.div-1">div<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#div.div-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1433,14 +1433,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.d.ts:21</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="divNoNan" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>div<wbr/>No<wbr/>Nan</span><a href="#divNoNan" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="divNoNan.divNoNan-1">div<wbr/>No<wbr/>Nan<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#divNoNan.divNoNan-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="divNoNan.divNoNan-1">div<wbr/>No<wbr/>Nan<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#divNoNan.divNoNan-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1451,14 +1451,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.d.ts:21</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dot" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>dot</span><a href="#dot" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="dot.dot-1">dot<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor-1.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><a href="#dot.dot-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="dot.dot-1">dot<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor-1.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><a href="#dot.dot-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1469,7 +1469,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <a href="Tensor-1.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></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.d.ts:5</li></ul></aside></li></ul></section>
@@ -1489,7 +1489,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="equal" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>equal</span><a href="#equal" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="equal.equal-1">equal<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#equal.equal-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="equal.equal-1">equal<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#equal.equal-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1500,7 +1500,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.d.ts:5</li></ul></aside></li></ul></section>
@@ -1619,14 +1619,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="flatten" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>flatten</span><a href="#flatten" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="flatten.flatten-1">flatten<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">Tensor1D</span><a href="#flatten.flatten-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="flatten.flatten-1">flatten<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><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a><a href="#flatten.flatten-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
 <h4>T<span class="tsd-signature-symbol"> extends </span><a href="Tensor-1.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></section>
-<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Tensor1D</span></h4><aside class="tsd-sources">
+<h4 class="tsd-returns-title">Returns <a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.d.ts:21</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="floor" class="tsd-anchor"></a>
@@ -1650,7 +1650,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="floorDiv" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>floor<wbr/>Div</span><a href="#floorDiv" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="floorDiv.floorDiv-1">floor<wbr/>Div<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#floorDiv.floorDiv-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="floorDiv.floorDiv-1">floor<wbr/>Div<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#floorDiv.floorDiv-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1661,14 +1661,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.d.ts:5</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="gather" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>gather</span><a href="#gather" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="gather.gather-1">gather<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>, indices<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</span>, axis<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" data-tsd-kind="Type parameter">T</span><a href="#gather.gather-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="gather.gather-1">gather<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>, indices<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a>, axis<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" data-tsd-kind="Type parameter">T</span><a href="#gather.gather-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1681,7 +1681,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <li>
 <h5>this: <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h5></li>
 <li>
-<h5>indices: <a href="Tensor-1.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-type">TensorLike</span></h5></li>
+<h5>indices: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li>
 <li>
 <h5><code class="tsd-tag ts-flagOptional">Optional</code> axis: <span class="tsd-signature-type">number</span></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
@@ -1690,7 +1690,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="greater" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>greater</span><a href="#greater" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="greater.greater-1">greater<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#greater.greater-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="greater.greater-1">greater<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#greater.greater-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1701,14 +1701,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.d.ts:5</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="greaterEqual" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>greater<wbr/>Equal</span><a href="#greaterEqual" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="greaterEqual.greaterEqual-1">greater<wbr/>Equal<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#greaterEqual.greaterEqual-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="greaterEqual.greaterEqual-1">greater<wbr/>Equal<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#greaterEqual.greaterEqual-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1719,7 +1719,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.d.ts:5</li></ul></aside></li></ul></section>
@@ -1834,7 +1834,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="less" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>less</span><a href="#less" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="less.less-1">less<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#less.less-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="less.less-1">less<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#less.less-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1845,14 +1845,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.d.ts:5</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="lessEqual" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>less<wbr/>Equal</span><a href="#lessEqual" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="lessEqual.lessEqual-1">less<wbr/>Equal<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#lessEqual.lessEqual-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="lessEqual.lessEqual-1">less<wbr/>Equal<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#lessEqual.lessEqual-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -1863,7 +1863,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.d.ts:5</li></ul></aside></li></ul></section>
@@ -1990,7 +1990,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="logicalAnd" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>logical<wbr/>And</span><a href="#logicalAnd" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="logicalAnd.logicalAnd-1">logical<wbr/>And<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#logicalAnd.logicalAnd-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="logicalAnd.logicalAnd-1">logical<wbr/>And<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#logicalAnd.logicalAnd-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2001,7 +2001,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.d.ts:5</li></ul></aside></li></ul></section>
@@ -2021,7 +2021,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="logicalOr" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>logical<wbr/>Or</span><a href="#logicalOr" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="logicalOr.logicalOr-1">logical<wbr/>Or<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#logicalOr.logicalOr-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="logicalOr.logicalOr-1">logical<wbr/>Or<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#logicalOr.logicalOr-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2032,14 +2032,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.d.ts:5</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="logicalXor" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>logical<wbr/>Xor</span><a href="#logicalXor" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="logicalXor.logicalXor-1">logical<wbr/>Xor<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#logicalXor.logicalXor-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="logicalXor.logicalXor-1">logical<wbr/>Xor<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#logicalXor.logicalXor-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2050,14 +2050,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.d.ts:5</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="matMul" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>mat<wbr/>Mul</span><a href="#matMul" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="matMul.matMul-1">mat<wbr/>Mul<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</span>, transposeA<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span>, transposeB<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><a href="Tensor-1.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><a href="#matMul.matMul-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="matMul.matMul-1">mat<wbr/>Mul<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a>, transposeA<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span>, transposeB<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><a href="Tensor-1.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><a href="#matMul.matMul-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2068,7 +2068,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li>
 <li>
 <h5><code class="tsd-tag ts-flagOptional">Optional</code> transposeA: <span class="tsd-signature-type">boolean</span></h5></li>
 <li>
@@ -2105,7 +2105,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
@@ -2123,7 +2123,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="maximum" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>maximum</span><a href="#maximum" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="maximum.maximum-1">maximum<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#maximum.maximum-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="maximum.maximum-1">maximum<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#maximum.maximum-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2134,7 +2134,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.d.ts:5</li></ul></aside></li></ul></section>
@@ -2181,7 +2181,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="minimum" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>minimum</span><a href="#minimum" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="minimum.minimum-1">minimum<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#minimum.minimum-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="minimum.minimum-1">minimum<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#minimum.minimum-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2192,7 +2192,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.d.ts:5</li></ul></aside></li></ul></section>
@@ -2219,7 +2219,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="mod" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>mod</span><a href="#mod" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="mod.mod-1">mod<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#mod.mod-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="mod.mod-1">mod<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#mod.mod-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2230,14 +2230,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.d.ts:5</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="mul" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>mul</span><a href="#mul" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="mul.mul-1">mul<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#mul.mul-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="mul.mul-1">mul<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#mul.mul-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2248,7 +2248,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.d.ts:5</li></ul></aside></li></ul></section>
@@ -2295,7 +2295,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="notEqual" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>not<wbr/>Equal</span><a href="#notEqual" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="notEqual.notEqual-1">not<wbr/>Equal<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#notEqual.notEqual-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="notEqual.notEqual-1">not<wbr/>Equal<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#notEqual.notEqual-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2306,7 +2306,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.d.ts:5</li></ul></aside></li></ul></section>
@@ -2374,7 +2374,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
@@ -2396,7 +2396,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="pow" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>pow</span><a href="#pow" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="pow.pow-1">pow<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>exp<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#pow.pow-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="pow.pow-1">pow<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>exp<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#pow.pow-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2407,14 +2407,14 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>exp: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>exp: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.d.ts:5</li></ul></aside></li></ul></section>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="prelu" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>prelu</span><a href="#prelu" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="prelu.prelu-1">prelu<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>alpha<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">TensorLike</span><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><a href="#prelu.prelu-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="prelu.prelu-1">prelu<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>alpha<span class="tsd-signature-symbol">: </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><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><a href="#prelu.prelu-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2425,7 +2425,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>alpha: <span class="tsd-signature-type">TensorLike</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h5></li></ul></div>
+<h5>alpha: <a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.d.ts:5</li></ul></aside></li></ul></section>
@@ -2559,7 +2559,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
@@ -2581,7 +2581,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
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@@ -2684,20 +2684,20 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="separableConv2d" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>separable<wbr/>Conv2d</span><a href="#separableConv2d" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="separableConv2d.separableConv2d-1">separable<wbr/>Conv2d<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>depthwiseFilter<span class="tsd-signature-symbol">: </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike4D</span>, pointwiseFilter<span class="tsd-signature-symbol">: </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span>, strides<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">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span>, pad<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">&quot;valid&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;same&quot;</span>, dilation<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">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span>, dataFormat<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">&quot;NHWC&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;NCHW&quot;</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><a href="#separableConv2d.separableConv2d-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="separableConv2d.separableConv2d-1">separable<wbr/>Conv2d<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>depthwiseFilter<span class="tsd-signature-symbol">: </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike4D</span>, pointwiseFilter<span class="tsd-signature-symbol">: </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a>, strides<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">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span>, pad<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">&quot;valid&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;same&quot;</span>, dilation<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">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span>, dataFormat<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">&quot;NHWC&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;NCHW&quot;</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><a href="#separableConv2d.separableConv2d-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
 <ul class="tsd-type-parameters">
 <li>
-<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Tensor3D</span></h4></li></ul></section>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/Tensor3D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor3D</a></h4></li></ul></section>
 <div class="tsd-parameters">
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
 <h5>depthwiseFilter: <a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike4D</span></h5></li>
 <li>
-<h5>pointwiseFilter: <a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike</span></h5></li>
+<h5>pointwiseFilter: <a href="../types/Tensor4D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor4D</a><span class="tsd-signature-symbol"> | </span><a href="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li>
 <li>
 <h5>strides: <span class="tsd-signature-type">number</span><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></h5></li>
 <li>
@@ -2920,7 +2920,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="squaredDifference" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>squared<wbr/>Difference</span><a href="#squaredDifference" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="squaredDifference.squaredDifference-1">squared<wbr/>Difference<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#squaredDifference.squaredDifference-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="squaredDifference.squaredDifference-1">squared<wbr/>Difference<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#squaredDifference.squaredDifference-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -2931,7 +2931,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.d.ts:21</li></ul></aside></li></ul></section>
@@ -3030,7 +3030,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="sub" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>sub</span><a href="#sub" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="sub.sub-1">sub<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#sub.sub-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="sub.sub-1">sub<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>b<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#sub.sub-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -3041,7 +3041,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>b: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>b: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.d.ts:5</li></ul></aside></li></ul></section>
@@ -3270,7 +3270,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="unsortedSegmentSum" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>unsorted<wbr/>Segment<wbr/>Sum</span><a href="#unsortedSegmentSum" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
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+<li class="tsd-signature tsd-anchor-link" id="unsortedSegmentSum.unsortedSegmentSum-1">unsorted<wbr/>Segment<wbr/>Sum<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>, segmentIds<span class="tsd-signature-symbol">: </span><a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike1D</span>, numSegments<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" data-tsd-kind="Type parameter">T</span><a href="#unsortedSegmentSum.unsortedSegmentSum-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -3283,7 +3283,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <li>
 <h5>this: <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h5></li>
 <li>
-<h5>segmentIds: <span class="tsd-signature-type">Tensor1D</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike1D</span></h5></li>
+<h5>segmentIds: <a href="../types/Tensor1D.html" class="tsd-signature-type" data-tsd-kind="Type alias">Tensor1D</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TensorLike1D</span></h5></li>
 <li>
 <h5>numSegments: <span class="tsd-signature-type">number</span></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
@@ -3327,7 +3327,7 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="where" class="tsd-anchor"></a>
 <h3 class="tsd-anchor-link"><span>where</span><a href="#where" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
 <ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
-<li class="tsd-signature tsd-anchor-link" id="where.where-1">where<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>condition<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</span>, x<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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-type">TensorLike</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><a href="#where.where-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-signature tsd-anchor-link" id="where.where-1">where<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>condition<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a>, x<span class="tsd-signature-symbol">: </span><a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#where.where-1" aria-label="Permalink" class="tsd-anchor-icon"><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"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
 <li class="tsd-description">
 <section class="tsd-panel tsd-type-parameters">
 <h4>Type Parameters</h4>
@@ -3338,9 +3338,9 @@ parameter, so can not use a user-defined size to create the buffer.</p>
 <h4 class="tsd-parameters-title">Parameters</h4>
 <ul class="tsd-parameter-list">
 <li>
-<h5>condition: <a href="Tensor-1.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-type">TensorLike</span></h5></li>
+<h5>condition: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li>
 <li>
-<h5>x: <a href="Tensor-1.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-type">TensorLike</span></h5></li></ul></div>
+<h5>x: <a href="Tensor-1.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="../types/TensorLike.html" class="tsd-signature-type" data-tsd-kind="Type alias">TensorLike</a></h5></li></ul></div>
 <h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
 <ul>
 <li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@4.2.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.d.ts:21</li></ul></aside></li></ul></section>
diff --git a/typedoc/functions/empty.html b/typedoc/functions/empty.html
index 01f8f19b..29317254 100644
--- a/typedoc/functions/empty.html
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 <li class="tsd-kind-type-alias"><a href="../types/Point.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Point</a></li>
 <li class="tsd-kind-type-alias"><a href="../types/Race.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Race</a></li>
 <li class="tsd-kind-type-alias"><a href="../types/SegmentationEnum.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Segmentation<wbr/>Enum</a></li>
+<li class="tsd-kind-type-alias"><a href="../types/Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
+<li class="tsd-kind-type-alias"><a href="../types/Tensor2D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor2D</a></li>
+<li class="tsd-kind-type-alias"><a href="../types/Tensor3D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor3D</a></li>
 <li class="tsd-kind-type-alias"><a href="../types/Tensor4D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor4D</a></li>
+<li class="tsd-kind-type-alias"><a href="../types/TensorLike.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor<wbr/>Like</a></li>
 <li class="tsd-kind-type-alias"><a href="../types/WarmupEnum.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Warmup<wbr/>Enum</a></li>
 <li class="tsd-kind-variable"><a href="../variables/defaults.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-variable)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-32-path"></rect><path d="M11.106 16L8.85 7.24H9.966L11.454 13.192C11.558 13.608 11.646 13.996 11.718 14.356C11.79 14.708 11.842 14.976 11.874 15.16C11.906 14.976 11.954 14.708 12.018 14.356C12.09 13.996 12.178 13.608 12.282 13.192L13.758 7.24H14.85L12.582 16H11.106Z" fill="var(--color-text)" id="icon-32-text"></path></svg>defaults</a></li>
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diff --git a/typedoc/index.html b/typedoc/index.html
index 29f0df55..258b434d 100644
--- a/typedoc/index.html
+++ b/typedoc/index.html
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 <a href="types/Race.html" class="tsd-index-link tsd-kind-type-alias"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg><span>Race</span></a>
 <a href="types/SegmentationEnum.html" class="tsd-index-link tsd-kind-type-alias"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg><span>Segmentation<wbr/>Enum</span></a>
+<a href="types/Tensor1D.html" class="tsd-index-link tsd-kind-type-alias"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg><span>Tensor1D</span></a>
+<a href="types/Tensor2D.html" class="tsd-index-link tsd-kind-type-alias"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg><span>Tensor2D</span></a>
+<a href="types/Tensor3D.html" class="tsd-index-link tsd-kind-type-alias"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg><span>Tensor3D</span></a>
 <a href="types/Tensor4D.html" class="tsd-index-link tsd-kind-type-alias"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg><span>Tensor4D</span></a>
+<a href="types/TensorLike.html" class="tsd-index-link tsd-kind-type-alias"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg><span>Tensor<wbr/>Like</span></a>
 <a href="types/WarmupEnum.html" class="tsd-index-link tsd-kind-type-alias"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg><span>Warmup<wbr/>Enum</span></a>
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 <section class="tsd-index-section">
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 <li class="tsd-kind-type-alias"><a href="types/Point.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Point</a></li>
 <li class="tsd-kind-type-alias"><a href="types/Race.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Race</a></li>
 <li class="tsd-kind-type-alias"><a href="types/SegmentationEnum.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Segmentation<wbr/>Enum</a></li>
+<li class="tsd-kind-type-alias"><a href="types/Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
+<li class="tsd-kind-type-alias"><a href="types/Tensor2D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor2D</a></li>
+<li class="tsd-kind-type-alias"><a href="types/Tensor3D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor3D</a></li>
 <li class="tsd-kind-type-alias"><a href="types/Tensor4D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor4D</a></li>
+<li class="tsd-kind-type-alias"><a href="types/TensorLike.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor<wbr/>Like</a></li>
 <li class="tsd-kind-type-alias"><a href="types/WarmupEnum.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Warmup<wbr/>Enum</a></li>
 <li class="tsd-kind-variable"><a href="variables/defaults.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-32-path"></use><use href="#icon-32-text"></use></svg>defaults</a></li>
 <li class="tsd-kind-variable"><a href="variables/env-1.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-32-path"></use><use href="#icon-32-text"></use></svg>env</a></li>
diff --git a/typedoc/modules/Tensor.html b/typedoc/modules/Tensor.html
index fce238b8..2993561b 100644
--- a/typedoc/modules/Tensor.html
+++ b/typedoc/modules/Tensor.html
@@ -250,7 +250,11 @@
 <li class="tsd-kind-type-alias"><a href="../types/Point.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Point</a></li>
 <li class="tsd-kind-type-alias"><a href="../types/Race.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Race</a></li>
 <li class="tsd-kind-type-alias"><a href="../types/SegmentationEnum.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Segmentation<wbr/>Enum</a></li>
+<li class="tsd-kind-type-alias"><a href="../types/Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
+<li class="tsd-kind-type-alias"><a href="../types/Tensor2D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor2D</a></li>
+<li class="tsd-kind-type-alias"><a href="../types/Tensor3D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor3D</a></li>
 <li class="tsd-kind-type-alias"><a href="../types/Tensor4D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor4D</a></li>
+<li class="tsd-kind-type-alias"><a href="../types/TensorLike.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor<wbr/>Like</a></li>
 <li class="tsd-kind-type-alias"><a href="../types/WarmupEnum.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Warmup<wbr/>Enum</a></li>
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diff --git a/typedoc/types/AnyCanvas.html b/typedoc/types/AnyCanvas.html
index c7a5de25..3efbe2f8 100644
--- a/typedoc/types/AnyCanvas.html
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@@ -112,7 +112,11 @@
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index d6c33231..738da145 100644
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index 0a540290..cbd2176f 100644
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+<li class="tsd-kind-type-alias"><a href="Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
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index cdc01e3c..8235089b 100644
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index 8887d48b..18a27e53 100644
--- a/typedoc/types/Finger.html
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diff --git a/typedoc/types/FingerCurl.html b/typedoc/types/FingerCurl.html
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--- a/typedoc/types/FingerCurl.html
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diff --git a/typedoc/types/FingerDirection.html b/typedoc/types/FingerDirection.html
index d3e229a8..bb61a11a 100644
--- a/typedoc/types/FingerDirection.html
+++ b/typedoc/types/FingerDirection.html
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+<li class="tsd-kind-type-alias"><a href="Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
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diff --git a/typedoc/types/Gender.html b/typedoc/types/Gender.html
index af3bc442..52d657a2 100644
--- a/typedoc/types/Gender.html
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+<li class="tsd-kind-type-alias"><a href="Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
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index 89c3a8b4..f939b46a 100644
--- a/typedoc/types/GestureResult.html
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+<li class="tsd-kind-type-alias"><a href="Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
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+<li class="tsd-kind-type-alias"><a href="Tensor3D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor3D</a></li>
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+<li class="tsd-kind-type-alias"><a href="TensorLike.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor<wbr/>Like</a></li>
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diff --git a/typedoc/types/HandGesture.html b/typedoc/types/HandGesture.html
index b3b96b2f..160514a7 100644
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+<li class="tsd-kind-type-alias"><a href="Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
+<li class="tsd-kind-type-alias"><a href="Tensor2D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor2D</a></li>
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+<li class="tsd-kind-variable"><a href="../variables/defaults.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-variable)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-32-path"></rect><path d="M11.106 16L8.85 7.24H9.966L11.454 13.192C11.558 13.608 11.646 13.996 11.718 14.356C11.79 14.708 11.842 14.976 11.874 15.16C11.906 14.976 11.954 14.708 12.018 14.356C12.09 13.996 12.178 13.608 12.282 13.192L13.758 7.24H14.85L12.582 16H11.106Z" fill="var(--color-text)" id="icon-32-text"></path></svg>defaults</a></li>
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+<li class="tsd-kind-function"><a href="../functions/empty.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>empty</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/types/WarmupEnum.html b/typedoc/types/WarmupEnum.html
index ad238fed..929edccc 100644
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+<li class="tsd-kind-type-alias"><a href="Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
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index 7a28afae..9ae2f2b9 100644
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+<li class="tsd-kind-type-alias"><a href="../types/Tensor1D.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Tensor1D</a></li>
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diff --git a/types/human.d.ts b/types/human.d.ts
index 3da7ec4a..b9e74aeb 100644
--- a/types/human.d.ts
+++ b/types/human.d.ts
@@ -2189,6 +2189,10 @@ export declare enum Rank {
     R6 = "R6"
 }
 
+declare interface RecursiveArray<T extends any> {
+    [index: number]: T | RecursiveArray<T>;
+}
+
 declare const registerLoadRouter: (loudRouter: IORouter) => void;
 
 declare const registerSaveRouter: (loudRouter: IORouter) => void;
@@ -2537,6 +2541,15 @@ export declare class Tensor<R extends Rank = Rank> implements TensorInfo {
     variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
 }
 
+/** @doclink Tensor */
+export declare type Tensor1D = Tensor<Rank.R1>;
+
+/** @doclink Tensor */
+export declare type Tensor2D = Tensor<Rank.R2>;
+
+/** @doclink Tensor */
+export declare type Tensor3D = Tensor<Rank.R3>;
+
 /** @doclink Tensor */
 export declare type Tensor4D = Tensor<Rank.R4>;
 
@@ -2596,6 +2609,9 @@ declare interface TensorInfo_2 {
     dtype: DataType;
 }
 
+/** @docalias TypedArray|Array */
+export declare type TensorLike = TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[];
+
 /** Model training configuration. */
 declare interface TrainingConfig {
     /** Optimizer used for the model training. */
@@ -2615,6 +2631,8 @@ declare interface TrainingConfig {
     };
 }
 
+declare type TypedArray = Float32Array | Int32Array | Uint8Array;
+
 declare type Url = string | io.IOHandler | io.IOHandlerSync;
 
 declare type UrlIOHandler<T extends Url> = T extends string ? io.IOHandler : T;